Neural Network Weight Quantization Using Lattice Vector Codebooks
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Solution Overview
Problem
Existing CNN implementations face challenges such as reduced accuracy, increased computational burden, and memory bottlenecks due to weight quantization and large memory footprints, which are exacerbated in edge computing applications like IoT devices.
Innovation Solution
A method involving lattice vector quantization (LVQ) is applied to compress and decompress neural network weights, using a regularization term to maintain accuracy while reducing memory footprint and computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If weight quantization is applied to reduce memory footprint, then memory requirements are reduced, but accuracy of the CNN deteriorates
Solution Approach 1:
The patent applies lattice vector quantization with a regularization term that modifies the quantization parameters to preserve accuracy. The regularization term ΩL(u) = (σ²/2)∑ᵢ₌₁ᵈ exp(-||uᵢ - cwᵢ||²/(2σ²)) adjusts the quantization process by amplifying weight values closer to lattice points, thereby maintaining CNN accuracy while achieving compression.
2Measurement precision
If fine tuning processing is applied to recover accuracy loss, then accuracy is improved, but computational burden increases
Solution Approach 1:
The patent incorporates accuracy preservation directly into the quantization process through the regularization term, eliminating the need for separate fine-tuning steps. By pre-adjusting the weight quantization with ΩL(u), the method recovers accuracy loss during the compression phase itself, avoiding additional computational burden.
3Measurement precision
If large numbers of weight parameters are used, then CNN processing accuracy is improved, but memory storage requirements increase
Solution Approach 1:
The patent transforms the storage requirement from millions of individual weight parameters to a compressed representation using lattice vector quantization. By representing weights as quantized vectors with regularization, the method maintains processing accuracy while dramatically reducing memory storage from O(N) to O(K) where K is the codebook size.
4Productivity
If dot product operations between weights and feature vectors are performed, then CNN processing is achieved, but computation cost increases
Solution Approach 1:
The patent changes the weight representation to quantized lattice vectors, which enables more efficient computation. The regularization term ensures that quantized weights maintain their effectiveness in dot product operations while reducing precision requirements, thereby lowering computation cost through reduced bit-width operations and better hardware utilization.
Data Source
AI summary
A method, comprising: providing an ANN processing stage having a plurality of processing layers with respective parameters including at least one set of weight parameters, at least one input, resp. output, activation parameter and at least one activation function parameter; setting to an integer value a dimensional parameter of a lattice having a plurality of lattice points and identified by a set of basis vectors; selecting a set of weight parameters of a respective processing layer; vectorizing the selected set of weight parameters producing a set of weight vectors arranged as items of a matrix of weight vectors; normalizing the matrix of weight vectors; applying lattice vector quantization, LVQ, processing to the matrix of normalized weight vectors, producing a codebook of codewords; indexing by encoding codewords of the codebook as a function of the lattice, producing respective tuples of indices. The normalization comprises solving an optimization problem having a first term configured to provide normalized weight values approximating at least output, as a function of at least one input, activation parameter, and a regularization term ΩL, configured to amplify weight values having a short distance from the lattice points of the lattice L.