Neural Network Processor Macro Instructions for Efficiency
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Solution Overview
Problem
Current neural network processors require complex sequences of low-level instructions for executing neural network operations, leading to inefficiencies in processing and performance, especially in feature extraction and classification tasks.
Innovation Solution
A neural network processor that executes macro instructions, which encompass multiple low-level operations, allowing for higher-level operations such as convolution, matrix multiplication, and data movement, and supports variable precision and log number format to optimize processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If complex sequences of low-level instructions are used for neural network operations, then detailed control over processing is achieved, but processing efficiency and performance deteriorate
Solution Approach 1:
The patent combines multiple low-level instructions into single macro instructions that represent complete neural network operations (convolution, matrix multiplication, pooling). This merging eliminates the need for complex instruction sequences while maintaining detailed control, directly resolving the contradiction between control precision and processing efficiency
Solution Approach 2:
The macro instructions are pre-configured with complete operation parameters including data sets, operations, and modes of operation. This preliminary preparation allows the processor to execute complex neural network operations in fewer cycles without requiring detailed control sequences during execution, improving productivity while preserving operational control
2Ease of operation
If multiple low-level instructions are executed for each neural network operation, then precise control over each step is maintained, but the number of processing cycles increases
Solution Approach 1:
Multiple low-level instructions are merged into single macro instructions that encapsulate complete neural network operations. This reduces the total number of processing cycles required while maintaining precise control through the macro instruction's embedded parameters for data sets, operations, and modes
Solution Approach 2:
All control parameters for neural network operations are predetermined and embedded in the macro instructions before execution. This preliminary configuration eliminates the need for step-by-step control during processing, reducing processing cycles while preserving control precision through the pre-specified operation parameters
Data Source
AI summary
Implementing a neural network can include receiving a macro instruction for implementing the neural network within a control unit of a neural network processor. The macro instruction can indicate a first data set, a second data set, a macro operation for the neural network, and a mode of operation for performing the macro operation. The macro operation can be automatically initiated using a processing unit of the neural network processor by applying the second data set to the first data set based on the mode of operation.