RNS Matrix Multiplier Normalization for Neural Network Efficiency
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Solution Overview
Problem
Current hardware matrix multipliers using binary arithmetic face inefficiencies in high-precision matrix multiplication, particularly in neural networks, due to increased power consumption and resource requirements, which limits their performance in applications like autonomous vehicles and cloud-based AI.
Innovation Solution
The implementation of a matrix multiplier using residue number arithmetic (RNS) with a systolic array of multiplier-accumulators, which processes data with high precision through carry-free RNS arithmetic, allowing for parallel processing of digit slices and efficient dot product summation, followed by word normalization, resulting in reduced resource usage and increased speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If binary arithmetic is used in hardware matrix multipliers, then implementation is straightforward and compatible with standard technology, but power consumption increases and resource requirements grow for high-precision operations
Solution Approach 1:
The patent changes the arithmetic parameter from binary to residue number system (RNS), fundamentally altering how calculations are performed. This parameter change enables carry-free arithmetic operations that consume less power while maintaining high precision, directly resolving the contradiction between ease of manufacture and power consumption.
Solution Approach 2:
The patent substitutes the traditional binary arithmetic mechanism with residue number system arithmetic. This substitution replaces the carry-propagation mechanism inherent in binary systems with a carry-free RNS mechanism, reducing power consumption while maintaining computational functionality.
2Ease of manufacture
If binary arithmetic is used in hardware matrix multipliers, then standard technology can be applied, but resource requirements increase for high-precision matrix multiplication
Solution Approach 1:
The patent changes the arithmetic parameter from binary to residue number system (RNS), fundamentally altering how calculations are performed. This parameter change enables carry-free arithmetic operations that consume less power, directly resolving the contradiction between ease of manufacture and power consumption.
Solution Approach 2:
The patent substitutes the traditional binary arithmetic mechanism with residue number system arithmetic. This substitution replaces the carry-propagation mechanism inherent in binary systems with a carry-free RNS mechanism, reducing power consumption while maintaining computational functionality.
3Device complexity
If traditional binary matrix multipliers are used, then design is simpler, but speed is slower for high-precision operations
Solution Approach 1:
The patent segments the high-precision multiplication process into multiple parallel RNS digit processing channels. Each channel processes a specific RNS digit independently using simple logic, avoiding complex carry propagation. This segmentation enables high-speed parallel processing while keeping individual processing elements simple.
Solution Approach 2:
The patent transitions from sequential binary arithmetic to parallel RNS arithmetic by adding a dimensional aspect - processing multiple RNS digits simultaneously in parallel channels. This dimensional change from sequential to parallel processing dramatically increases speed while maintaining design simplicity through modular architecture.
Data Source
AI summary
Arithmetic circuits and methods that perform efficient matrix multiplication for hardware acceleration of neural networks, machine learning, web search and other applications are disclosed herein. Various arrays of multiplier-accumulators may be coupled to form a matrix multiplier which processes data using high precision, fixed point residue number arithmetic.