Semiconductor Device Pipelining AI Operations
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Solution Overview
Problem
Existing semiconductor systems face inefficiencies in general-purpose computing and limited software support for neural processing units (NPUs), leading to suboptimal performance and increased latency and power consumption in artificial intelligence operations, especially in resource-constrained environments.
Innovation Solution
A semiconductor device and system that utilizes dedicated hardware for AI computations, employing pipelining techniques and alternating memory usage to efficiently perform AI operations, with separate memories for storing data before and after AI operations, and incorporating pre- and post-processing units for input and output data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If general-purpose processors (GPU/NPU) are used for neural network operations, then AI computation capability is provided, but resource utilization efficiency deteriorates and latency increases
Solution Approach 1:
The patent segments the neural network processing into distinct operational phases (data input, weight application, accumulation, output generation) and implements dedicated hardware units for each phase. The operator is divided into multiple sub-operators, each handling specific computational tasks, thereby improving resource utilization efficiency while maintaining AI computation capability.
Solution Approach 2:
The patent implements dynamic resource allocation through a control unit that manages data flow between memories and operators based on operational requirements. The system dynamically switches between different operational modes (training vs. inference, different neural network layers) to optimize resource utilization for varying computational demands.
2Productivity
If general-purpose processors are used for neural network operations, then AI computation is enabled, but power consumption increases
Solution Approach 1:
The patent implements self-service mechanisms where the dedicated operator hardware performs computations autonomously without requiring general-purpose processor intervention. The control unit manages data movement and operational coordination internally, reducing the energy overhead associated with general-purpose processor execution and enabling efficient AI computation with lower power consumption.
3Ease of operation
If separate memories are used for data before and after AI operations, then data management efficiency is improved, but hardware complexity increases
Solution Approach 1:
The patent implements multi-functional memory structures that serve multiple purposes: storing input data, intermediate results, and output data. The first and second memories are utilized across different operational phases and neural network layers, reducing the need for separate dedicated storage for each data type while maintaining efficient data management through unified memory control.
4Speed
If pipelining is used for AI operations, then processing speed is improved, but control complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-fetching data into the first memory before computational operations begin, and by pre-organizing weight data in the third memory according to operational requirements. This preliminary data preparation reduces control complexity during the actual pipelined execution, as data availability is ensured in advance for each processing stage.
Data Source
AI summary
The described techniques provide efficient semiconductor device configurations and improved processes for facilitating artificial intelligence operations. In an example, a semiconductor device may be configured with first memory for storing data before an artificial intelligence operation and second memory for storing data after an artificial intelligence operation. The use of the first memory and the second memory for storing data before and after the artificial intelligence operation, respectively, may support a simplified layout for a semiconductor device to facilitate artificial intelligence operations with minimal hardware configurations and limited software. In addition, the use of the first memory and the second memory for storing data before and after the artificial intelligence operation, respectively, may allow for processing when the domains of input data and output data are different.