Tunstall Code Decoder Architecture for Parallel Symbol Decoding
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Solution Overview
Problem
Existing decoders for Tunstall codes, particularly in the context of resource-constrained devices, suffer from inefficient decoding processes, leading to high computational complexity and slow inference speeds due to variable-length codewords, which are difficult to decode in parallel.
Innovation Solution
Implement a Tunstall decoder comprising a sub-decoder, symbol memory, and controller to efficiently decode Tunstall codes using a Tunstall tree structure, allowing for parallel processing of multiple symbols and reducing decoding complexity to O(n) by comparing input codewords with stored codewords at each level of the tree.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If Fixed-to-Variable (F2V) coding methods such as Huffman coding are used to compress quantized weights, then compression ratio is improved, but decoding complexity increases significantly and decoding speed decreases
Solution Approach 1:
The patent inverts the conventional F2V coding approach by using Variable-to-Fixed (V2F) Tunstall coding. Instead of encoding fixed-length symbols into variable-length codewords for compression, the system encodes variable-length symbol sequences into fixed-length codewords. This inversion enables parallel decoding by allowing the entire codeword to be processed simultaneously rather than requiring sequential bit-by-bit processing, thus maintaining high compression ratios while dramatically improving decoding speed.
Solution Approach 2:
The patent segments the decoding process into multiple parallel processing units that can simultaneously decode different fixed-length codewords. By dividing the decoding task into independent parallel segments, the system achieves high throughput while maintaining the compression benefits of entropy coding. Each processing unit handles fixed-length codewords independently, enabling parallel execution without complex synchronization.
2Quantity of substance
If variable-length codewords are used in F2V coding methods, then compression efficiency is improved, but parallel processing becomes difficult and decoding complexity increases to O(n·k)
Solution Approach 1:
The patent fundamentally inverts the coding direction from F2V to V2F. Conventional F2V methods map fixed-length symbols to variable-length codewords, making parallel processing difficult. The patent instead maps variable-length symbol sequences to fixed-length codewords, enabling simple parallel processing with O(n) complexity while preserving compression efficiency through the entropy-coded structure of the V2F mapping.
Solution Approach 2:
The patent changes the fundamental parameter of codeword length from variable to fixed. By transforming the codeword representation from variable-length to fixed-length, the system enables efficient parallel processing and reduces decoding complexity from O(n·k) to O(n), while maintaining compression efficiency through the statistical properties exploited by the Tunstall coding scheme.
3Quantity of substance
If conventional F2V decoding methods are used, then compression is achieved, but memory access requirements increase and energy consumption rises due to sequential processing
Solution Approach 1:
The patent inverts the coding paradigm to enable more efficient memory access patterns. By using fixed-length V2F codewords instead of variable-length F2V codewords, the system can load and process multiple codewords simultaneously from memory, reducing memory access latency and energy consumption. The fixed-length structure allows for predictable memory access patterns that can be optimized for parallel processing architectures.
Solution Approach 2:
The patent performs preliminary encoding offline to create the V2F codebook mapping, which is then stored for efficient online decoding. This preliminary action allows the online decoding phase to simply perform table lookups and comparisons rather than complex sequential processing, significantly reducing real-time computational requirements and energy consumption during inference.
4Area of stationary object
If resource-constrained devices with limited memory are used, then device area is reduced, but memory capacity for storing weights becomes insufficient for complex neural networks
Solution Approach 1:
The patent changes the bit representation parameters by using V2F Tunstall coding to compress weight values into fixed-length codewords with fewer bits. This parameter transformation reduces the memory capacity requirement by achieving higher compression ratios while maintaining numerical precision through the entropy-coded structure, allowing complex neural networks to fit within limited on-chip memory resources.
Solution Approach 2:
The patent inverts the conventional approach of storing raw or quantized weight values by instead storing compressed V2F codewords. This inversion of the storage representation achieves significant memory capacity reduction, enabling resource-constrained devices to store larger neural network models within limited memory resources while maintaining inference accuracy.
Data Source
AI summary
A decoder for decoding a codeword of a Tunstall code is provided, including: a sub-decoder configured to receive an input codeword of the Tunstall code to the decoder and output a decoded symbol of the input codeword; a symbol memory configured to receive and store the decoded symbol of the input codeword from the sub-decoder; and a controller configured to control the symbol memory to output one or more decoded symbols stored in the symbol memory. The sub-decoder includes: a node memory configured to store, for a plurality of nodes of a Tunstall tree of the Tunstall code corresponding to a first level of the Tunstall tree, a plurality of codewords of the Tunstall code assigned to the plurality of nodes, respectively; and a comparator configured to compare the input codeword with the plurality of codewords assigned to the plurality of nodes corresponding to the first level of the Tunstall tree received from the node memory and produce the decoded symbol of the input codeword with respect to the first level of the Tunstall tree based on the comparison. Another decoder for decoding a codeword of a Tunstall code is also provided, including: a symbol memory comprising a plurality of memory entries, each memory entry having stored therein one or more decoded symbols of a codeword of the Tunstall code corresponding to the memory entry; and a controller configured to receive an input codeword of the Tunstall code to the decoder and control the symbol memory to output the one or more decoded symbols stored in one of the plurality of memory entries corresponding to the input codeword.