Vector Dequantization Hardware for Dynamic NN Weight Lookup
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Solution Overview
Problem
Current artificial neural networks (NNs) and deep neural networks (DNNs) face inefficiencies in processing data due to high latency and memory usage, particularly in reading neuron weight values from memory, which can be costly in terms of time and power, and reducing precision to save memory compromises accuracy.
Innovation Solution
The implementation of vector quantization of neuron weight values, which allows for more weight values to be represented in a given memory space by converting contiguous segments into vectors and using indices to reference weight values from a lookup table, reducing memory transfer and maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If weight values are stored in general memory (DRAM), then memory capacity is sufficient, but reading weight values requires significant time and power
Solution Approach 1:
The patent segments weight values into codebooks organized by layer and neuron, allowing selective loading of only the required weight segments into local memory rather than loading entire weight matrices. This segmentation enables efficient memory hierarchy utilization where frequently accessed weight segments are cached locally while full weight data remains in general memory.
2Speed
If weight values are cached in local memory (SRAM), then reading speed improves, but memory cost increases and size is limited
Solution Approach 1:
The patent implements a universal weight storage and retrieval system that can handle multiple codebooks and layers through a single hardware architecture. The weight storage unit is designed to accommodate variable codebook sizes and vector lengths across different network layers, eliminating the need for separate dedicated memory structures for each layer and reducing overall hardware complexity.
3Quantity of substance
If precision of weight values is reduced (e.g., 32-bit to 16-bit), then memory requirements decrease, but results accuracy deteriorates
Solution Approach 1:
The patent changes the representation parameters of weight values by organizing them into codebooks with variable codebook sizes and vector lengths. This parameter change allows the system to store more weight values in the same memory space through efficient packing, achieving memory reduction without compromising the precision of individual weight values. The codebook structure enables flexible allocation of memory resources while maintaining full precision representation.
4Quantity of substance
If vector quantization is used to store more weight values in local memory, then memory efficiency improves, but hardware complexity increases
Solution Approach 1:
The patent implements dynamic vector quantization where the codebook size and vector length can be adjusted based on the specific requirements of each neural network layer. This dynamic configuration allows the hardware to adapt to different data dimensions and precision requirements without requiring multiple fixed-architecture units, thereby reducing overall hardware complexity while maintaining high memory efficiency.
Data Source
AI summary
The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as memory data management of a NN/DNN. Using vector quantization of neuron weight values, the processing of data by neurons can be optimize the number of operations as well as memory utilization to enhance the overall performance of a NN/DNN. Operatively, one or more contiguous segments of weight values can be converted into one or more vectors of arbitrary length and each of the one or more vectors can be assigned an index. The generated indexes can be stored in an exemplary vector quantization lookup table and retrieved by exemplary fast weight lookup hardware at run time on the flyas part of an exemplary data processing function of the NN as part of an inline de-quantization operation to obtain needed one or more neuron weight values.