Dynamic Neural Network Layer Compression for Resource Constraints
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Solution Overview
Problem
Neural network designers face a tradeoff between lossy and lossless compression methods, where lossy compression reduces quality but saves memory and bandwidth, while lossless compression maintains quality but consumes more resources, necessitating an adaptive compression system that optimizes neural network performance based on system conditions.
Innovation Solution
A method that applies a mixture of lossless and lossy compression to neural network layers based on a compression ratio and system events, such as bandwidth, power consumption, and thermal conditions, using a layers compression map determined by neural network heuristics and sparsity estimates to configure noise-sensitive layers for high-quality compression and others for efficient lossy compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy compression is used to reduce memory and bandwidth consumption, then resource usage decreases, but output quality and inference accuracy deteriorate
Solution Approach 1:
The system dynamically adjusts compression parameters based on system conditions (power state, thermal state, bandwidth availability). The compression ratio and methodology are not fixed but adapt in real-time to optimize the balance between resource consumption and accuracy, allowing the system to use lossy compression when resources are constrained and lossless or no compression when resources are abundant.
Solution Approach 2:
Different compression strategies are applied to different layers of the neural network based on their sensitivity to compression. The system identifies which layers are more tolerant of lossy compression and applies higher compression ratios to those layers, while using lossless or no compression on sensitive layers, thereby optimizing the overall accuracy-resource tradeoff.
2Manufacturing precision
If lossless compression is used to maintain output quality, then inference accuracy is preserved, but memory and bandwidth consumption increase
Solution Approach 1:
The system transitions from static compression settings to dynamic adjustment based on system conditions. When power is abundant, thermal conditions are good, and bandwidth is available, the system uses lossless compression or no compression to maximize accuracy. When resources are constrained, it dynamically switches to lossy compression to reduce resource usage while maintaining acceptable accuracy.
Solution Approach 2:
The system changes compression parameters (compression ratio, compression methodology) based on system conditions. It monitors power state, thermal state, and bandwidth availability to determine the optimal compression settings, adjusting these parameters in real-time to balance accuracy and resource consumption.
3Use of energy by moving object
If compression is applied to reduce resource consumption, then power and bandwidth usage decrease, but thermal conditions may worsen due to decompression overhead
Solution Approach 1:
The system monitors thermal conditions and dynamically adjusts compression settings accordingly. When thermal conditions deteriorate, the system can reduce compression ratios or switch from lossy to lossless compression to minimize decompression overhead and heat generation, while still maintaining resource efficiency when conditions are favorable.
Data Source
AI summary
An apparatus of operating a neural network is configured to compress one or more of activations or weights in one or more layer of the neural network. The activations and/or weights may be compressed based on a compression ratio or a system event. The system event may be a bandwidth condition, a power condition, a debug condition, a thermal condition or the like. The apparatus may operate the neural network to compute an inference based on the compressed activations or the compressed weights.