Neural Network Weight Huffman Decoding for Faster Compressed Inference
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Solution Overview
Problem
Existing data compression methods for neural networks, particularly in the field of neural networks, face inefficiencies in reducing memory consumption and improving energy efficiency, especially when dealing with variable length encoding of model weights.
Innovation Solution
A method utilizing Huffman coding to encode and decode neural network weights by counting consecutive zeros before the first '1' bit, converting this prefix into a predetermined code, and concatenating it with the subsequent bits, while using a Huffman tree for decoding to maintain a fixed length, thereby optimizing compression and decompression processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If variable length encoding is used to compress neural network weights, then data compression ratio is improved, but decoding complexity and time consumption increase
Solution Approach 1:
The patent pre-generates a mapping table that stores the correspondence between compressed codes and original weight values before decoding operations. This mapping table is constructed in advance based on the specific characteristics of neural network weights (such as the distribution of leading zeros), allowing the decoder to directly lookup decompressed values without performing complex variable-length decoding algorithms during actual inference, thus resolving the contradiction between compression ratio and decoding complexity
Solution Approach 2:
The patent creates a simplified copy of the decoding logic in the form of a pre-computed mapping table. Instead of implementing the full variable-length decoding algorithm (which would be complex and time-consuming), the system uses this copied structure (mapping table) that replicates the essential functionality of the decoder, enabling fast direct access to decompressed values while maintaining the benefits of variable-length encoding
2Quantity of substance
If variable length encoding is used for weight compression, then memory usage is reduced, but operation speed decreases
Solution Approach 1:
The mapping table is pre-computed and stored in memory before inference operations begin. During actual neural network operations, the system performs fast table lookups instead of executing variable-length decoding algorithms, significantly improving operation speed while maintaining the memory savings achieved through compression
Solution Approach 2:
The patent uses a lightweight mapping table structure that requires minimal memory resources compared to storing full-precision weights. The mapping table acts as a compact intermediary that enables fast operations without requiring large amounts of memory, achieving a balance between memory efficiency and operation speed
3Ease of manufacture
If conventional compression methods are used, then implementation simplicity is maintained, but energy efficiency is insufficient
Solution Approach 1:
The patent applies compression specifically to the weight parameters of neural networks, which are stationary and do not change during inference. By targeting only this specific component (local quality) rather than compressing all data types uniformly, the system achieves significant energy savings in weight loading and memory operations while keeping the implementation relatively simple through the use of pre-computed mapping tables
Data Source
AI summary
Disclosed are an encoder and decoder configured to encode and decode a weight and an operating method of the encoder and the decoder. An operating method of a decoding device includes: receiving a compressed weight including a preceding code and subsequent bits following the preceding code; and decoding the compressed weight by applying the preceding code of the compressed weight to a Huffman tree, wherein the Huffman tree decodes the preceding code, and wherein the decoded preceding code is joined with the subsequent bits to form a decompressed version of the compressed weight.