[Paper] Single-Stage Huffman Encoder for ML Compression

Source: arXiv - 2601.10673v1

Overview

The paper introduces a single‑stage Huffman encoder that can compress tensors on‑the‑fly during LLM training and inference without the usual three‑step overhead (frequency analysis, codebook generation, and codebook transmission). By re‑using a fixed codebook derived from the average symbol distribution of earlier batches, the authors achieve near‑optimal compression while dramatically cutting latency—an important win for die‑to‑die communication in multi‑accelerator setups.

Key Contributions

• Fixed‑codebook Huffman scheme: Eliminates per‑batch frequency counting and codebook exchange, reducing compute and communication latency.
• Empirical analysis of tensor statistics: Shows that activations and weight shards in the Gemma 2B model share highly similar probability distributions across layers and devices.
• Near‑optimal compression: Achieves compression within 0.5 % of traditional per‑shard Huffman coding and within 1 % of the Shannon limit.
• Practical on‑the‑fly implementation: Demonstrates that the encoder can be integrated into existing training pipelines with negligible overhead.
• Open‑source reference implementation (provided by the authors) for easy adoption in PyTorch/DeepSpeed environments.

Methodology

1. Statistical profiling – Run a few warm‑up batches through the model and record the symbol frequencies (e.g., 8‑bit quantized values) for each tensor type (activations, gradients, weights).
2. Average distribution extraction – Compute an average probability distribution across all observed batches, assuming that later batches will follow a similar pattern.
3. Fixed Huffman tree construction – Using the averaged distribution, build a single Huffman codebook once and store it on every accelerator.
4. Single‑stage encoding – During actual training/inference, tensors are directly encoded with the pre‑computed codebook; no extra analysis or transmission is needed.
5. Evaluation – Measure compression ratios and latency on the Gemma 2B model across multiple GPUs/TPUs, comparing against (a) per‑shard Huffman, (b) naive 8‑bit quantization, and (c) the theoretical Shannon bound.

Results & Findings

• The fixed codebook incurs <2 % extra latency, a dramatic improvement over the 12 % overhead of traditional Huffman.
• Compression quality stays within 0.5 % of per‑shard Huffman and 1 % of the Shannon optimum, confirming that the average distribution is a reliable proxy.
• End‑to‑end training throughput on an 8‑GPU cluster improved by ~6 % because the communication bottleneck was alleviated.

Practical Implications

• Accelerator‑to‑accelerator communication: Developers can drop the codebook exchange step entirely, making collective operations (e.g., all‑reduce, broadcast) faster and more predictable.
• Framework integration: The approach can be wrapped as a custom torch.distributed compressor or a DeepSpeed communication hook, requiring only a one‑time initialization.
• Cost savings: Reduced network traffic translates to lower cloud egress fees and better utilization of high‑speed interconnects (NVLink, InfiniBand).
• Latency‑sensitive serving: For inference pipelines that shard model weights across chips, the encoder enables on‑the‑fly compression without adding noticeable latency, opening the door to larger models on the same hardware budget.
• Hardware‑agnostic: Since the encoder works on standard 8‑bit tensors, it can be deployed on GPUs, TPUs, and emerging AI accelerators without custom ASIC support.

Limitations & Future Work

• Distribution drift: The fixed codebook assumes stationary symbol statistics; drastic changes in data distribution (e.g., domain shift) could degrade compression. Adaptive refresh mechanisms are left for future exploration.
• Model‑specific profiling: The study focuses on Gemma 2B; other architectures (e.g., vision transformers) may exhibit different statistical patterns, requiring separate profiling runs.
• Quantization granularity: The method currently targets 8‑bit tensors; extending to mixed‑precision (e.g., 4‑bit) or floating‑point formats would need additional research.
• Security & robustness: Fixed codebooks could be a side‑channel if an attacker knows the exact mapping; integrating lightweight encryption or obfuscation could be a next step.

Bottom line: By swapping a three‑step Huffman pipeline for a single, pre‑computed codebook, this work delivers near‑optimal lossless compression with negligible latency—an attractive tool for anyone building large‑scale LLM training or serving stacks.

Authors

• Aditya Agrawal
• Albert Magyar
• Hiteshwar Eswaraiah
• Patrick Sheridan
• Pradeep Janedula
• Ravi Krishnan Venkatesan
• Krishna Nair
• Ravi Iyer

Paper Information

• arXiv ID: 2601.10673v1
• Categories: cs.LG
• Published: January 15, 2026
• PDF: Download PDF