AI model hardware adaptation and core reasoning computing system and method

Abstract

The invention belongs to the technical field of artificial intelligence, and particularly relates to an AI model hardware adaptation and core reasoning calculation subsystem and method. Comprising a dynamic feature weight adjustment module, an operator-instruction set co-occurrence mapping engine, a three-dimensional balance prediction feedback unit, a heterogeneous feature conflict resolution module and a cross-scene feature migration module. According to the method, hardware and model feature coupling weights are dynamically distributed, operator and instruction set co-occurrence mapping and iteration are constructed, three-dimensional indexes are predicted and adjusted in advance, heterogeneous hardware conflicts are eliminated, and reuse adaptation experience is migrated, so that end-to-end adaptation optimization is achieved; according to the method, heterogeneous hardware and multi-model scenes can be fully covered, the hardware calculation potential is fully released, core indexes are prospectively balanced, the collaborative performance loss is reduced, the cross-platform adaptation compatibility and landing efficiency are remarkably improved, and the method is suitable for multiple fields such as edge calculation, automatic driving and intelligent terminals.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence technology, specifically an AI model hardware adaptation and core inference operator system and method. Background Technology

[0002] With the rapid development of artificial intelligence technology, deep learning models are increasingly widely used in fields such as computer vision, natural language processing, and speech recognition, placing higher demands on the real-time performance, efficiency, and compatibility of model inference. The inference performance of AI models depends not only on the model's own structural design but also on the characteristics of the operating platform. Therefore, the adaptation and optimization of AI models to hardware and the performance tuning of core inference operators have become key aspects restricting the practical application of AI technology.

[0003] In existing technologies, solutions for AI model hardware adaptation and operator optimization mainly include the following categories: 1. Static hardware adaptation solution: This solution achieves model-hardware matching by pre-writing customized operator code or configuring adaptation parameters for specific hardware platforms. However, this solution requires developing adaptation logic separately for each hardware platform, resulting in long adaptation cycles, poor reusability, and an inability to respond to dynamic needs during hardware load changes or model inference stages. When hardware load fluctuates or the model switches to computationally intensive or storage-intensive stages, the adaptation effect drops significantly, severely limiting scenario adaptability.

[0004] 2. Single-dimensional operator optimization techniques: While optimizing core inference operators using single techniques such as quantization compression, sparse pruning, and loop unrolling can improve inference performance to some extent, they do not establish a dynamic relationship between operators and hardware instruction sets. In existing solutions, the mapping relationship between operators and instruction sets is mostly a fixed configuration, which cannot be iterated in real time with hardware firmware updates and instruction set expansions. This makes it difficult to fully release the native computing potential of the hardware, and the operator execution efficiency always remains a bottleneck.

[0005] 3. Accuracy-Performance-Power Consolidation Strategy: Existing solutions mostly adopt a post-adjustment mode of "optimize first, then verify," that is, optimize the operator first, and then test whether the accuracy, performance, and power consumption indicators meet the standards. If they do not meet the standards, the optimization parameters are adjusted in reverse. This mode has optimization lag. When the imbalance of indicators is detected, the model has already been running in an unbalanced state for a period of time, and the adjustment process is prone to indicator oscillations, making it difficult to achieve a dynamic balance among the three. This is especially prominent in edge low-power devices, where the problems of excessive power consumption or excessive accuracy loss are particularly prominent.

[0006] 4. Heterogeneous Hardware Collaborative Adaptation Solution: For multi-hardware collaborative inference scenarios, existing technologies mostly adopt simple task allocation strategies without considering the differences in characteristics of different hardware, such as feature conflicts caused by computing power, storage bandwidth, and power consumption constraints. When multiple heterogeneous hardware participates in inference at the same time, problems such as competition for computing resources, incompatible data transmission protocols, and power consumption quota conflicts are likely to occur. Furthermore, there is a lack of effective conflict resolution mechanisms, resulting in a significant overall performance loss in collaborative adaptation and failing to leverage the collaborative advantages of heterogeneous hardware.

[0007] In addition, existing solutions also suffer from insufficient cross-platform and cross-framework compatibility. Most adaptation solutions only support a single AI framework, such as TensorFlow or PyTorch, or a few hardware types. When faced with a brand-new hardware platform or a custom model structure, the initial adaptation takes too long, and the adaptation effect is difficult to guarantee.

[0008] In summary, existing AI model hardware adaptation and core inference operator optimization schemes suffer from technical defects such as rigid adaptation, insufficient release of hardware potential, lagging three-dimensional balancing, inability to effectively resolve heterogeneous collaboration conflicts, and poor compatibility. These shortcomings make it difficult to meet the comprehensive requirements of AI model inference accuracy, performance, and power consumption in complex scenarios. There is an urgent need for a technical solution that can dynamically adapt, efficiently optimize, and achieve global balance to solve the above problems. Summary of the Invention

[0009] To overcome the shortcomings of existing technologies and solve the aforementioned technical problems, this invention proposes an AI model hardware adaptation and core inference operator system and method.

[0010] The technical solution adopted by this invention to solve its technical problem is to propose an AI model hardware adaptation and core inference operator system, including: Dynamic feature weight adjustment module: used to monitor the computing unit load status, storage status and AI model inference stage of the target hardware in real time, and dynamically allocate the coupling weights of the computing unit characteristics, storage component characteristics and model structure characteristics of the hardware. Operator-instruction set symbiotic mapping engine: Based on the dynamic update log of the hardware instruction set and the computational characteristics of operators, it constructs the symbiotic mapping relationship between the two and iterates in real time; Three-dimensional balance prediction feedback unit: It uses a lightweight prediction model to detect potential imbalances in accuracy, performance, and power consumption in advance and generates pre-adjustment instructions; Heterogeneous feature conflict resolution module: For multi-hardware collaborative adaptation scenarios, it identifies and dynamically resolves feature conflicts between hardware. Collaborative Control Center: Coordinates the above modules to achieve end-to-end adaptation and optimization processes.

[0011] Furthermore, the dynamic feature weight adjustment module includes a hardware load perception submodule and a model stage identification submodule; the hardware load perception submodule collects the computing unit occupancy rate and memory bandwidth utilization rate in real time; the model stage identification submodule is used to distinguish between the feature extraction, feature fusion, and output prediction stages of model inference; and dynamically adjusts the weights according to the rule that the higher the hardware load, the greater the weight of hardware characteristics, and the greater the weight of model structure features in the model computation-intensive stage.

[0012] Furthermore, the operator-instruction set symbiotic mapping engine includes an operator feature extractor, an instruction set dynamic parser, and a symbiotic relationship learning unit. The operator feature extractor is used to extract features of computational complexity, data reuse rate, and parallelism. The instruction set dynamic parser tracks instruction set expansions brought about by hardware firmware updates. The symbiotic relationship learning unit transfers historical mapping experience to the new instruction set through transfer learning, thereby improving the matching accuracy between operators and instruction sets to over 96% and the utilization rate of hardware computing units to over 90%.

[0013] Furthermore, the three-dimensional balance prediction feedback unit adopts a time-series prediction model, based on the accuracy fluctuation value, performance fluctuation value, and power consumption fluctuation value of the past 100 inferences, to predict the indicator trend of the next stage 50 milliseconds in advance; when it is predicted that any indicator will exceed the preset threshold, a pre-adjustment instruction is generated, which includes the quantization bit width adjustment range and the operator fusion quantity suggestion, so that the indicator overshoot probability is reduced by more than 70%.

[0014] Furthermore, it also includes a cross-scenario feature transfer module, which extracts the commonalities of adaptation features from different hardware platforms and different model types to build a general feature transfer model; when adapting to new hardware or models, it transfers optimization experience from similar scenarios, reducing the time for the first adaptation by more than 75%, and achieving more than 90% of the optimal solution in the initial adaptation.

[0015] A method for hardware adaptation and core inference operators for AI models includes the following steps: Step 1: Dynamic feature weight allocation: Real-time monitoring of hardware computing unit load, storage status and model inference stage, dynamically adjusting the coupling weights of hardware computing unit characteristics, storage component characteristics and model features; Step 2: Operator-Instruction Set Co-occurrence Mapping Construction: Extract operator features and hardware instruction set dynamic features, construct and iterate the co-occurrence mapping relationship; Step 3: Three-dimensional balance prediction and pre-adjustment: By predicting the time series, the risk of imbalance is detected in advance, and pre-adjustment instructions are generated; Step 4: Heterogeneous Feature Conflict Resolution: Identify and dynamically resolve feature conflicts during multi-hardware collaborative adaptation; Step 5: Adaptation Result Output and Iteration: Output the optimized adaptation scheme and continuously iterate the parameters of each module based on the running data.

[0016] Furthermore, the specific rules for dynamic feature weight allocation in step 1 are as follows: when the hardware load rate is less than 50%, the sum of the weights of the hardware's computing unit characteristics and storage component characteristics is 30% to 40%, and the model feature weight is 60% to 70%; when the hardware load rate is greater than 80%, the sum of the weights of the hardware's computing unit characteristics and storage component characteristics is 60% to 70%, and the model feature weight is 30% to 40%; when the model is in the feature fusion stage, the model feature weight is increased by an additional 10% to 15% to ensure the adaptation accuracy in computationally intensive scenarios.

[0017] Furthermore, the iterative mechanism of the symbiotic mapping relationship described in step 2 is as follows: whenever a hardware instruction set update is detected, including the addition of new instructions and changes in instruction execution efficiency, or the addition of new operator types, the symbiotic relationship update is triggered; the update process compares the similarity between the new features and historical features, reuses mapping experience with a similarity higher than 85%, thereby reducing the update time by more than 60%, while ensuring that the execution efficiency of the new mapping is not lower than 95% of the historical optimal solution.

[0018] Furthermore, the time series prediction model described in step 3 adopts a lightweight LSTM architecture. The input dimensions include accuracy loss value, inference latency, power consumption value, and hardware temperature. The output dimension is the predicted value of the indicators for the next three inference cycles. The execution priority of the pre-adjustment instruction is higher than that of the real-time optimization instruction. When the pre-adjustment and real-time adjustment conflict, the pre-adjustment instruction takes precedence to ensure the forward-looking nature of the three-dimensional balance.

[0019] Furthermore, the specific method for resolving heterogeneous feature conflicts in step 4 is as follows: identify the conflict type, including computing resource contention, data transmission protocol incompatibility, and power consumption constraint conflict; adopt time-slice round-robin scheduling for computing resource contention, enable intermediate conversion interface for protocol incompatibility, and dynamically allocate power quota for power consumption conflict; the conflict resolution time does not exceed 20 milliseconds, and the overall performance loss of multi-hardware collaborative adaptation is controlled within 5%.

[0020] The beneficial effects of this invention are as follows: 1. The AI ​​model hardware adaptation and core inference operator system and method described in this invention achieves comprehensive coverage of AI model adaptation scenarios under heterogeneous hardware platforms by dynamically adjusting the coupling weights of hardware computing unit and storage component characteristics and model structural features, and constructing a symbiotic mapping between operators and hardware instruction sets and iterating in real time. At the same time, it fully releases the native computing potential of hardware, with hardware computing unit utilization reaching over 90% and operator and instruction set matching accuracy improving to over 96%.

[0021] 2. The AI ​​model hardware adaptation and core inference operator system and method described in this invention uses a lightweight time-series prediction model to anticipate potential imbalances in accuracy, performance, and power consumption and generate pre-adjustment instructions, thereby achieving a forward-looking dynamic balance among the three and avoiding overshoot and oscillation of indicators. While keeping the accuracy loss within a reasonable range, the inference latency is reduced by more than 35%, the power consumption is reduced by more than 20%, and the probability of indicator overshoot is reduced by more than 70%.

[0022] 3. The AI ​​model hardware adaptation and core inference operator system and method described in this invention dynamically identifies and resolves feature conflicts such as computing resource competition and protocol incompatibility during multi-hardware collaborative adaptation. Combined with cross-scenario feature transfer and reuse of similar adaptation experience, it achieves overall performance loss of multi-heterogeneous hardware collaborative inference within 5%, and reduces the initial adaptation time of new hardware or models by more than 75%, significantly improving cross-platform and cross-framework adaptation compatibility and deployment efficiency. Attached Figure Description

[0023] The invention will now be further described with reference to the accompanying drawings.

[0024] Figure 1 This is a system block diagram of the present invention; Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0025] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments and accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] like Figure 1 As shown, this invention provides an AI model hardware adaptation and core inference operator system, which constructs a dynamic adaptation and optimization architecture through multi-module collaboration. The core includes: Dynamic feature weight adjustment module: Real-time monitoring of the target hardware's computing unit load status, storage status, and AI model inference stage, dynamically allocating the coupling weights of the hardware's computing unit characteristics, storage component characteristics, and model structure characteristics to adapt to the needs of different hardware loads and model computing scenarios. Operator-Instruction Set Symbiotic Mapping Engine: Based on the dynamic update log of the hardware instruction set and the computational features of the operator, the symbiotic mapping relationship between the two is constructed and iterated in real time. Through operator feature extraction, dynamic parsing of the instruction set and learning of the symbiotic relationship, the accurate matching between the operator and the native hardware instruction set is achieved. Three-dimensional balance prediction feedback unit: It adopts a lightweight time-series prediction model, which can detect potential imbalance risks in accuracy, performance and power consumption in advance based on historical inference data, and generate pre-adjustment instructions including quantization bit width adjustment range and operator fusion number suggestions to achieve a forward-looking dynamic balance among the three. Heterogeneous feature conflict resolution module: For multi-hardware collaborative adaptation scenarios, it identifies types of conflicts such as computing resource competition, incompatible data transmission protocols, and power consumption constraint conflicts, and dynamically resolves conflicts through strategies such as time slice round-robin scheduling, intermediate conversion interfaces, and dynamic power quotas. Collaborative Control Center: Coordinates the above modules to achieve end-to-end adaptation and optimization processes, while integrating a cross-scenario feature transfer module to extract common adaptation features of different hardware platforms and different model types, construct a general feature transfer model, and reuse similar adaptation experience.

[0027] As a specific embodiment of the present invention, the dynamic feature weight adjustment module includes a hardware load perception submodule and a model stage identification submodule, with a weight adjustment granularity of one percent and a response latency of no more than ten milliseconds; the operator-instruction set co-occurrence mapping engine reuses historical mapping experience through transfer learning, improving the matching accuracy to over 96%; the temporal prediction model inference latency of the three-dimensional balanced prediction feedback unit is no more than five milliseconds, and the overshoot probability of the index is reduced by more than 70%; the conflict resolution time of the heterogeneous feature conflict resolution module is no more than twenty milliseconds, and the performance loss of multi-hardware collaboration is controlled within five percent.

[0028] like Figure 2 As shown, an AI model hardware adaptation and core inference operator method is implemented based on the aforementioned AI model hardware adaptation and core inference operator system. Specific steps include: Step 1: Dynamic feature weight allocation: Real-time monitoring of hardware computing unit load, storage status and model inference stage, and dynamically adjusting the coupling weight of hardware computing unit characteristics, storage component characteristics and model features according to the rule that "the higher the hardware load, the greater the weight of hardware characteristics, and the greater the weight of model structure features in the model computation intensive stage". Step 2: Operator-Instruction Set Co-existence Mapping Construction: Extract features such as computational complexity and data reuse rate of operators and dynamic features of hardware instruction sets to construct a co-existence mapping relationship. When an instruction set update or a new operator type is detected, the mapping is triggered to iterate in real time by comparing feature similarity with historical experience. Step 3: Three-dimensional balance prediction and pre-adjustment: By inputting features such as precision loss value, inference latency, power consumption value, and hardware temperature into the lightweight time series prediction model, the trend of indicators in multiple inference cycles in the future is predicted. When the predicted indicator will exceed the preset threshold, the pre-adjustment instruction is executed to avoid the risk of imbalance. Step 4: Heterogeneous Feature Conflict Resolution: Identify the types of feature conflicts during multi-hardware collaborative adaptation, and adopt targeted resolution strategies to solve problems such as computing resource contention, protocol incompatibility, and power consumption conflicts; Step 5: Adaptation Result Output and Iteration: Output the optimized adaptation scheme, and based on the running data of each inference, incrementally update the prediction model parameters and co-occurrence mapping relationship to continuously optimize the adaptation effect.

[0029] In one specific embodiment of the present invention, in step 1, when the hardware load rate is less than 50%, the hardware characteristic weight is 30% to 40%, and the model feature weight is 60% to 70%; when the hardware load rate is greater than 80%, the hardware characteristic weight is 60% to 70%, and the model feature weight is 30% to 40%; when the model is in the feature fusion stage, the model feature weight is increased by an additional 10% to 15%; in step 3, the execution priority of the pre-adjustment instruction is higher than that of the real-time optimization instruction, and the pre-adjustment instruction takes precedence in case of conflict; in step 4, conflicts are identified by the cosine similarity of feature vectors, and a similarity of less than 0.6 is considered a conflict.

[0030] To further illustrate the present invention, the following detailed description is provided in conjunction with specific embodiments. Those skilled in the art can understand the specific implementation path of the present invention based on these embodiments, ensuring the feasibility of the solution.

[0031] An explanation of the architecture, hardware and algorithm selection for an AI model hardware adaptation and core inference operator system: Core hardware platform selection and collaborative topology: Target hardware type: Covering heterogeneous hardware environments, including: CPU: Intel Xeon Gold 6348, 28 cores, 2.6GHz, supports AVX-512 instruction set; GPU: NVIDIA A100, 40GB HBM2, supports CUDA 11.0+ and Tensor Core; FPGA: Xilinx Alveo U280, supports OpenCL1.2, integrates 5880 DSP slices; Edge ASIC: Huawei Ascend 310B, 16 TOPS@INT8, power consumption 15W.

[0032] Collaborative topology: All hardware is interconnected through a PCIe 4.0 switch Mellanox SX1012. The CPU acts as the main controller, and the GPU, FPGA, and ASIC communicate with the CPU through PCIe links. The data transmission latency is ≤100ns, and direct memory access between hardware is supported.

[0033] Data acquisition hardware: Hardware probe: A performance counter with a PCIe 4.0 interface, compatible with Intel VTune Profiler, supporting real-time acquisition of computing unit utilization and memory bandwidth usage, with a sampling frequency of 1kHz and a data interface of PCIe 4.0 x8; Temperature sensor: TMP102 digital sensor with I2C interface, accuracy ±0.5℃, sampling period 10ms, connected to the main controller via I2C-to-PCIe bridge; Power consumption monitoring module: Keysight N6951A DC power consumption acquisition unit, with a range of 0-50W, a sampling accuracy of 0.1W, and communicates with the CPU via USB 3.0 with a data refresh cycle of 5ms.

[0034] Storage and communication components: Memory: DDR4-3200, 128GB capacity, 51.2GB / s bandwidth, supports quad-channel interleaving; Cache unit: Samsung 990 Pro NVMe SSD, 2TB capacity, sequential read and write speeds of 7450MB / s and 6900MB / s respectively, used to store optimization result cache and log data; Heterogeneous communication protocol: CPU and GPU use NVLink 4.0 with a bandwidth of 900GB / s; CPU and FPGA / ASIC use PCIe 4.0 x16 with a bandwidth of 32GB / s.

[0035] Core Algorithm and Software Selection: Dynamic feature weight adjustment module: Hardware load awareness algorithm: Based on a sliding window statistical filtering algorithm with a window size of 100ms, it uses mean filtering to remove transient impulse interference, is implemented in C++, runs in CPU kernel mode, and consumes ≤5% of CPU resources; Model Stage Recognition: A lightweight CNN classifier with an input layer, two convolutional layers (3×3 kernels), one fully connected layer, and an output layer, with 800,000 parameters; the training data consists of 100,000 image segments used by the model for computation, covering mainstream models such as ResNet and BERT, achieving a classification accuracy of ≥98%. Weight adjustment logic: Based on the dynamic allocation algorithm of the rule engine, implemented in Python, and calculated in real time through multi-threading; weight adjustment is triggered every 5% change in hardware load rate, with an adjustment granularity of 1% and a response latency of ≤10ms.

[0036] Operator-Instruction Set Symbiotic Mapping Engine: Operator Feature Extractor: A static analysis tool implemented in C++, based on the LLVM14.0 framework, extracting 12 features from convolution and matrix multiplication operators, including the number of input channels, kernel size, stride, data type, etc., with an extraction time of ≤2ms / operator; Instruction set dynamic parser: An instruction set analysis module based on LLVMIR, which parses hardware firmware update logs, such as CUDA driver update packages for NVIDIA GPUs, and extracts parameters such as latency (clock cycles), throughput (operands / cycle), and supported data types of newly added instructions; Symbiotic Relationship Learning Unit: The transfer learning model is a lightweight version of ResNet-18 with the last 3 convolutional layers removed; the training dataset contains 100,000 operator-instruction set mapping samples, covering CPU, GPU, and FPGA instruction sets; when transferring to new hardware, the learning rate is fine-tuned to 0.001, and the transfer accuracy is ≥90%.

[0037] Three-dimensional equilibrium prediction feedback unit: The time-series prediction model uses a lightweight LSTM. The input layer has 4-dimensional features: accuracy loss, inference latency, power consumption, and hardware temperature. There are 2 hidden layers with 64 neurons each. The output layer has 3-dimensional prediction values. The model has 120,000 parameters. The training data consists of 500,000 inference logs, including running data under different loads and model types. The loss function used is MAE (mean absolute error), with prediction error ≤5% and inference latency ≤5ms. Pre-adjustment instruction generator: Based on CART decision tree, depth ≤ 5 layers; preset thresholds are accuracy loss ≤ 2%, GPU power consumption ≤ 300W, inference latency ≤ 20ms; when exceeding the limits, it generates instructions such as quantization bit width adjustment range (1-8 bits) and number of operator fusions (2-6), with decision time ≤ 2ms.

[0038] Heterogeneous feature conflict resolution module: Conflict detection algorithm: Conflict detection based on cosine similarity of feature vectors. The hardware feature vector contains 5-dimensional parameters such as computing power and storage bandwidth. The model requirement vector contains 5-dimensional parameters such as computational load and data transmission load. A similarity of <0.6 is considered a conflict. The calculation time is ≤1ms. Resolution strategy executor: Real-time scheduler, implemented in C language, based on RT-Thread real-time operating system; time slice round-robin granularity of 1ms; intermediate conversion interface based on ProtocolBuffers 3.0 to achieve cross-hardware protocol compatibility, conversion latency ≤2ms.

[0039] Cross-scene feature transfer module: Feature commonality extraction: Autoencoder, input 15-dimensional features including hardware computing power, model parameter count, etc.; hidden layer 8-dimensional; output 15-dimensional reconstructed features; training data consists of 200,000 sets of adaptation cases with different hardware and models; loss function uses MSE (mean squared error), reconstruction error ≤5%; Transfer model: KNN-based similar scene matching, K=5, Euclidean distance is used to calculate similarity, matching time ≤10ms, similar scene recognition accuracy ≥92%.

[0040] A detailed implementation process for an AI model hardware adaptation and core inference operator method: Step 1: Dynamic Feature Weight Allocation Hardware status acquisition: The hardware load awareness submodule collects computing unit utilization rates through PCIe probes, such as CPU core utilization = 40%, GPUSM utilization = 85%, and memory bandwidth utilization = 35%, and updates the data every 10ms. The storage status is obtained through the Linux system's / proc / meminfo interface: memory usage = 25%, cache hit rate = 90%.

[0041] Model stage identification: The model stage identification submodule parses the ResNet-50 computation graph, identifying the conv1-conv4 layers (output feature map size gradually shrinks) as the feature extraction stage, the conv5 layer (multi-scale feature fusion) as the feature fusion stage, and the fc layer (output classification probability) as the output prediction stage. Dynamic weight adjustment: When the hardware load rate is less than 50%, the weight of hardware characteristics is 35% and the weight of model features is 65%; when the hardware load rate is greater than or equal to 50%, the weight of hardware characteristics is 65% and the weight of model features is 35%. If the model is in the feature fusion stage, the model feature weights are increased by an additional 12%; in actual use, the input hardware load rate is 40%, the model stage is feature fusion, and the output weights are 35% and 77%.

[0042] Step 2: Constructing the operator-instruction set co-occurrence map: Feature extraction: The operator feature extractor extracts features from the 3×3 convolution operator. The computational complexity is 256 (input channels) × 512 (output channels) × 3 × 3 = 1,179,648, and the data reuse rate is 0.8 (the input tensor is reused 8 times). The instruction set dynamic parser parses the NVIDIA A100 TensorCore instruction wmma_f32_16x16x16, obtains the parameter that supports a matrix size of 16×16×16, the execution latency is 2 clock cycles, and the throughput is 4096 operations / cycle.

[0043] Symbiotic mapping construction: For the 3×3 convolution operator, it is split into 16×16 submatrices (with edge padding to 16×16), mapped to the wmma_f32_16x16x16 instruction, and the single calculation time is reduced to 60% of the original solution; Iterative updates: A GPU firmware update was detected that added the fp8 precision wmma instruction wmma_fp8_16x16x16, triggering a mapping update. The feature similarity between fp8 and fp16 instructions is 90% (>85%). The original fp16 mapping logic is reused (only the data type conversion function is adjusted), and the update time is reduced from 100ms to 35ms (a reduction of 65%).

[0044] Step 3: Three-dimensional equilibrium prediction and pre-adjustment: Data input: Collect the accuracy loss (0.5%-1.2%), inference latency (8-12ms), power consumption (12-18W), and hardware temperature (55-65℃) of the past 100 inferences, and input them into the LSTM model after standardization; Forecast and Pre-adjustment: The LSTM predicts that the latency will rise to 15ms in the next 3 cycles (30ms) (exceeding the threshold of 12ms). The output pre-adjustment instruction is to reduce the quantization bit width of the convolution operator from 8 bits to 4 bits and fuse two consecutive ReLU activation operators. The pre-adjustment instruction is sent to the operator optimization module through the collaborative control center. After execution, the actual delay is 11ms (not exceeding the threshold), and the overshoot probability is reduced from 40% in the comparison scheme to 10% (a reduction of 75%).

[0045] Step 4: Heterogeneous feature conflict resolution: Conflict identification: In a multi-hardware collaborative scenario (CPU+FPGA), feature vector similarity calculation revealed that the CPU's computing resources were being occupied by the FPGA (similarity = 0.5 < 0.6, indicating a computing resource contention conflict); the CPU uses the TCP protocol to transmit data, while the FPGA only supports RDMA (indicating a protocol incompatibility conflict). Dissolve execution: Computational resource contention: The scheduler allocates time slices (CPU occupies 5ms → FPGA occupies 5ms), and after the conflict, CPU utilization increases from 60% to 85%; Protocol incompatibility: Enable intermediate conversion interface (TCP to RDMA), conversion latency = 1.5ms, data transmission success rate = 100%; The performance loss after resolution is 3% (≤5%), while the overall inference throughput is increased by 20%.

[0046] Step 5: Adaptation Result Output and Iteration: The output optimization scheme includes an operator quantization bit width configuration table (e.g., conv1: 4bit, conv2: 8bit), an instruction set mapping table (3×3 convolution → wmma_fp8 instruction), and a weight allocation strategy (35%: 65% when the load is <50%). The iterative mechanism generates a running log (including accuracy, latency, and power consumption) every 100 inferences, and updates the LSTM prediction model (learning rate = 0.0001) and co-occurrence mapping relationship (adding new samples to the training set) through incremental learning to continuously optimize the performance.

[0047] Comparative experimental design and effect verification: Experimental environment: 1. Test models: ResNet-50 (ImageNet2012 validation set, input 224×224×3), BERT-Base (GLUE dataset, sequence length 128); 2. Comparison of options: Option 1: NVIDIA TensorRT 8.6 (static operator optimization, enabling INT8 quantization); Option 2: ONNXRuntime 1.15 (multi-framework support, enable CUDAExecutionProvider); Option 3: The solution of this invention (hardware / algorithm configuration is the same as the above selection).

[0048] 3. Test environment configuration: Operating system: Ubuntu 20.04 LTS, kernel 5.4.0; Drivers: NVIDIA Driver 525.105.17, CUDA 11.8; Testing tools: NVIDIA Nsight Systems (performance analysis), PowerTool (power consumption monitoring).

[0049] 4. Evaluation metrics: Computing unit utilization (%), operator-instruction set matching accuracy (%), inference latency reduction rate (%, relative to unoptimized solution), power consumption reduction rate (%, relative to unoptimized solution), multi-hardware collaboration performance loss (%), first adaptation time (s, for the new Ascend 610 hardware).

[0050] Experimental results and analysis:

[0051] Conclusion: The proposed solution, through core technologies such as dynamic feature weight adjustment and operator-instruction set co-occurrence mapping, significantly outperforms existing solutions in all indicators. Furthermore, all effects have been verified through actual hardware configuration and algorithm implementation, demonstrating strong reproducibility.

[0052] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A hardware adaptation and core inference operator system for an AI model, characterized in that, include: Dynamic feature weight adjustment module: used to monitor the computing unit load status, storage status and AI model inference stage of the target hardware in real time, and dynamically allocate the coupling weights of the computing unit characteristics, storage component characteristics and model structure characteristics of the hardware. Operator-instruction set symbiotic mapping engine: Based on the dynamic update log of the hardware instruction set and the computational characteristics of operators, it constructs the symbiotic mapping relationship between the two and iterates in real time; Three-dimensional balance prediction feedback unit: It uses a lightweight prediction model to detect potential imbalances in accuracy, performance, and power consumption in advance and generates pre-adjustment instructions; Heterogeneous feature conflict resolution module: For multi-hardware collaborative adaptation scenarios, it identifies and dynamically resolves feature conflicts between hardware. Collaborative Control Center: Coordinates the above modules to achieve end-to-end adaptation and optimization processes.

2. The AI ​​model hardware adaptation and core inference operator system according to claim 1, characterized in that, The dynamic feature weight adjustment module includes a hardware load perception submodule and a model stage identification submodule; the hardware load perception submodule collects computing unit occupancy and memory bandwidth usage in real time; the model stage identification submodule is used to distinguish between the feature extraction, feature fusion, and output prediction stages of model inference. The weights are dynamically adjusted based on the rule that the higher the hardware load, the greater the weight of hardware characteristics, and the greater the weight of model structure features during the model computationally intensive stage.

3. The AI ​​model hardware adaptation and core inference operator system according to claim 1, characterized in that, The operator-instruction set symbiotic mapping engine includes an operator feature extractor, an instruction set dynamic parser, and a symbiotic relationship learning unit. The operator feature extractor is used to extract features of computational complexity, data reuse rate, and parallelism. The instruction set dynamic parser tracks instruction set expansions brought about by hardware firmware updates. The symbiotic relationship learning unit transfers historical mapping experience to the new instruction set through transfer learning.

4. The AI ​​model hardware adaptation and core inference operator system according to claim 1, characterized in that, The three-dimensional balance prediction feedback unit adopts a time-series prediction model, which predicts the trend of indicators in the next stage based on the accuracy fluctuation value, performance fluctuation value, and power consumption fluctuation value of the past 100 inferences. When it is predicted that any indicator will exceed the preset threshold, a pre-adjustment instruction is generated, which includes the quantization bit width adjustment range and the operator fusion number suggestion.

5. The AI ​​model hardware adaptation and core inference operator system according to claim 1, characterized in that, It also includes a cross-scenario feature transfer module, which extracts the commonalities of adaptation features across different hardware platforms and model types to build a general feature transfer model; and transfers optimization experience from similar scenarios when adapting to new hardware or models.

6. A method for AI model hardware adaptation and core inference operators, applicable to the AI ​​model hardware adaptation and core inference operator system described in any one of claims 1-5, characterized in that, Includes the following steps: Step 1: Dynamic feature weight allocation: Real-time monitoring of hardware computing unit load, storage status and model inference stage, dynamically adjusting the coupling weights of hardware computing unit characteristics, storage component characteristics and model features; Step 2: Operator-Instruction Set Co-occurrence Mapping Construction: Extract operator features and hardware instruction set dynamic features, construct and iterate the co-occurrence mapping relationship; Step 3: Three-dimensional balance prediction and pre-adjustment: By predicting the time series, the risk of imbalance is detected in advance, and pre-adjustment instructions are generated; Step 4: Heterogeneous Feature Conflict Resolution: Identify and dynamically resolve feature conflicts during multi-hardware collaborative adaptation; Step 5: Adaptation Result Output and Iteration: Output the optimized adaptation scheme and continuously iterate the parameters of each module based on the running data.

7. The AI ​​model hardware adaptation and core inference operator method according to claim 6, characterized in that, The specific rules for dynamic feature weight allocation in step 1 are as follows: when the hardware load rate is less than 50%, the sum of the weights of the hardware computing unit characteristics and storage component characteristics is 30% to 40%, and the model feature weight is 60% to 70%; when the hardware load rate is greater than 80%, the sum of the weights of the hardware computing unit characteristics and storage component characteristics is 60% to 70%, and the model feature weight is 30% to 40%; when the model is in the feature fusion stage, the model feature weight is increased by an additional 10% to 15%.

8. The AI ​​model hardware adaptation and core inference operator method according to claim 6, characterized in that, The iterative mechanism of the symbiotic mapping relationship described in step 2 is as follows: whenever a hardware instruction set update is detected, including the addition of new instructions and changes in instruction execution efficiency, or the addition of new operator types, the symbiotic relationship update is triggered.

9. The AI ​​model hardware adaptation and core inference operator method according to claim 6, characterized in that, The time series prediction model described in step 3 adopts a lightweight LSTM architecture. The input dimensions include accuracy loss value, inference latency, power consumption value, and hardware temperature. The output dimension is the predicted value of the indicator for the next three inference cycles. The execution priority of the pre-adjustment instruction is higher than that of the real-time optimization instruction. When the pre-adjustment and real-time adjustment conflict, the pre-adjustment instruction takes precedence.

10. The AI ​​model hardware adaptation and core inference operator method according to claim 6, characterized in that, The specific method for resolving heterogeneous feature conflicts in step 4 is as follows: identify the conflict type, including computing resource contention, data transmission protocol incompatibility, and power consumption constraint conflict; adopt time-slice round-robin scheduling for computing resource contention, enable intermediate conversion interface for protocol incompatibility, and dynamically allocate power quota for power consumption conflict.

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