Deep Neural Network Microprocessor Control Signals
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Solution Overview
Problem
Current computer architecture struggles to efficiently manage microprocessor operations due to reliance on slow and power-intensive software predictions, which are inadequate for complex workloads, and traditional approaches fail to holistically analyze the various elements influencing processor performance.
Innovation Solution
The implementation of deep neural networks (DNNs) that learn from computing workloads to generate control signals, predictions, and warnings, optimizing microprocessor operations by integrating sensor data, processing data, and DNN outputs to enhance performance, efficiency, and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software is used to evaluate and predict microprocessor conditions, then prediction capability is provided, but the system becomes slow and computationally expensive
Solution Approach 1:
The patent replaces traditional software-based prediction mechanisms with hardware-based prediction circuits integrated into the microprocessor. These hardware circuits directly monitor processor states and generate predictions without requiring software execution, thereby eliminating the computational overhead and speed limitations of software-based approaches while maintaining or improving prediction accuracy
Solution Approach 2:
The patent introduces dedicated prediction circuits as intermediary components between the processor execution units and the control logic. These circuits act as intermediaries that continuously monitor processor states (such as branch instructions, memory access patterns) and generate prediction signals that guide processor operations, enabling fast predictions without burdening the main processing pipeline
2Productivity
If additional processor cores are utilized to improve performance, then computational capability increases, but power consumption increases
Solution Approach 1:
The patent implements prediction circuits that perform preliminary analysis of processor workloads and predict future processor states before actual execution occurs. By predicting branch outcomes, memory access patterns, and instruction throughput in advance, the system can optimize resource allocation and avoid activating additional cores unless absolutely necessary, thereby maintaining high performance while reducing power consumption
Solution Approach 2:
The patent dynamically adjusts processor operating parameters (such as clock frequency, voltage levels, and core activation states) based on predictions generated by integrated circuits. When predictions indicate sufficient performance can be achieved through optimization of existing resources, the system avoids activating additional power-hungry cores, thus maintaining productivity while minimizing power consumption
3Device complexity
If traditional heuristic algorithms are used for specific processor operations, then simplicity is maintained, but holistic analysis of microprocessor elements is limited
Solution Approach 1:
The patent implements universal prediction circuits that can analyze multiple microprocessor elements simultaneously (branch units, memory controllers, execution pipelines) using a unified approach. These circuits are designed to monitor and predict various processor operations through common monitoring mechanisms, providing holistic analysis capability without requiring separate complex algorithms for each processor component
Solution Approach 2:
The patent divides the microprocessor into multiple monitorable segments (branch prediction units, cache control units, execution units) each with dedicated prediction circuits. Each segment maintains operational simplicity through specialized circuits, while the collective segmentation enables comprehensive holistic analysis of the entire processor system through coordinated prediction across all segments
Data Source
AI summary
Operations of computing devices are managed using one or more deep neural networks (DNNs), which may receive, as DNN inputs, data from sensors, instructions executed by processors, and/or outputs of other DNNs. One or more DNNs, which may be generative, can be applied to the DNN inputs to generate DNN outputs based on relationships between DNN inputs. The DNNs may include DNN parameters learned using one or more computing workloads. The DNN outputs may be, for example, control signals for managing operations of computing devices, predictions for use in generating control signals, warnings indicating an acceptable state is predicted, and/or inputs to one or more neural networks. The signals enhance performance, efficiency, and/or security of one or more of the computing devices. DNNs can be dynamically trained to personalize operations by updating DNN weights or other parameters.