ASIC Design of a Canonical Huffman Encoder for Computational Storage Drives

ABSTRACT Huffman coding is foundational to data compression algorithms. We propose an advanced application‐specific integrated circuit (ASIC) design for a canonical Huffman encoder, optimised for high throughput and low power consumption. Our design introduces a high‐speed sorting circuit, an efficient Huffman tree canonisation algorithm and an innovative entropy‐based compressibility prediction mechanism. Implemented using 12 nm CMOS technology, the proposed solution achieves a remarkable throughput of 4 GB/s and sub‐microsecond latency for 4 KB pages, outperforming existing x86 software implementations by nearly two orders of magnitude and surpassing state‐of‐the‐art hardware accelerators. This advancement significantly enhances data processing capabilities in computational storage drives (CSDs), providing a scalable and energy‐efficient data compression solution for modern data centres.

Authors

Institutions

Publication Details

Journal
Electronics Letters
Published
2025-01-01
DOI
https://doi.org/10.1049/ell2.70224
Citations
2
Primary Topic
Algorithms and Data Compression
Type
article
Field-Weighted Citation Impact
4.96
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ASIC Design of a Canonical Huffman Encoder for Computational Storage Drives

Aiguo Song, Tao Lü, Yunxin Huang, Yan Yang
2 citations
Electronics Letters
Algorithms and Data Compression
4.96
article

ASIC Design of a Canonical Huffman Encoder for Computational Storage Drives

Aiguo Song, Tao Lü, Yunxin Huang, Yan Yang
article en
2 citations

Abstract

ABSTRACT Huffman coding is foundational to data compression algorithms. We propose an advanced application‐specific integrated circuit (ASIC) design for a canonical Huffman encoder, optimised for high throughput and low power consumption. Our design introduces a high‐speed sorting circuit, an efficient Huffman tree canonisation algorithm and an innovative entropy‐based compressibility prediction mechanism. Implemented using 12 nm CMOS technology, the proposed solution achieves a remarkable throughput of 4 GB/s and sub‐microsecond latency for 4 KB pages, outperforming existing x86 software implementations by nearly two orders of magnitude and surpassing state‐of‐the‐art hardware accelerators. This advancement significantly enhances data processing capabilities in computational storage drives (CSDs), providing a scalable and energy‐efficient data compression solution for modern data centres.

Electronics LettersVol. 61(1)
Southeast University (CN)
Openalex Percentile: Top 6%
Algorithms and Data Compression
4.96
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.