LEXI: Lossless Exponent Coding for Efficient Inter-Chiplet Communication in Hybrid LLMs
LEXI reduces inter-chiplet communication latency by 33-45% using lossless exponent coding.
Key Findings
Methodology
LEXI uses Huffman coding for lossless compression of BF16 exponents. It compresses activations and caches on-the-fly, stores compressed weights for just-in-time decompression, reducing data transfer latency. Multi-lane LUT decoders ensure maximum link bandwidth with only 0.09% area and energy overhead.
Key Results
- LEXI reduces inter-chiplet communication latency by 33-45% and end-to-end inference latency by 30-35% on Jamba, Zamba, and Qwen models.
- Implemented using GF 22 nm technology with only 0.09% area and energy overhead.
- Achieves lossless compression while maintaining model accuracy on WikiText-2 and C4 datasets.
Significance
LEXI significantly reduces communication latency in large language models without affecting model accuracy, addressing the memory bandwidth bottleneck in data-intensive phases. This is crucial for hybrid LLMs requiring efficient inter-chiplet communication, facilitating the practical deployment of larger models.
Technical Contribution
LEXI introduces a lossless compression scheme for BF16 exponents, offering a more efficient hardware implementation compared to existing methods. By integrating compression codecs at NoC router I/O, it ensures minimal area and energy overhead.
Novelty
LEXI is the first to implement lossless exponent-only coding at full speed with negligible area and energy overhead. Unlike prior methods focusing solely on weight compression, LEXI also compresses activations and hybrid caches.
Limitations
- In extreme cases, exceeding 32 distinct exponent values may reduce compression efficiency.
- Requires specific hardware support for optimal performance.
Future Work
Future work could explore combining LEXI with other quantization and pruning techniques to further reduce communication overhead. Additionally, its adaptability to different hardware architectures could be studied.
AI Executive Summary
Inference latency in large language models (LLMs) is primarily affected by data movement overheads, especially in multi-chiplet accelerators. Existing solutions like quantization and pruning alleviate some issues but often at the cost of model accuracy. To address this, LEXI, a lossless exponent compression scheme based on Huffman coding, focuses on the exponent field of the BF16 format.
LEXI compresses activations and caches on-the-fly while storing compressed weights for just-in-time decompression, significantly reducing data transfer latency. Its design uses multi-lane LUT decoders to ensure maximum link bandwidth with only 0.09% area and energy overhead. Experiments show that LEXI reduces inter-chiplet communication latency by 33-45% and end-to-end inference latency by 30-35% on Jamba, Zamba, and Qwen models.
This technology significantly reduces communication latency in large language models without affecting model accuracy, addressing the memory bandwidth bottleneck in data-intensive phases. Future work could explore combining LEXI with other quantization and pruning techniques to further reduce communication overhead. Additionally, its adaptability to different hardware architectures could be studied.
Deep Analysis
Background
As large language models (LLMs) scale, their inference bottlenecks shift from computation to data movement, especially in multi-chiplet accelerators where cross-chip communication becomes a major hurdle. Existing methods like quantization and pruning alleviate some issues but often at the cost of model accuracy. The BF16 format is widely used for its stability in training and inference, and its exponent field's high compressibility offers potential for optimizing communication overhead.
Core Problem
Inference latency in large language models is primarily affected by data movement overheads, especially in multi-chiplet architectures where cross-chip communication becomes a major bottleneck. Existing solutions like quantization and pruning alleviate some issues but often at the cost of model accuracy. Therefore, reducing communication latency without affecting model accuracy is a critical challenge.
Innovation
LEXI introduces a lossless exponent compression scheme based on Huffman coding, focusing on the exponent field of the BF16 format. By compressing activations and caches on-the-fly and storing compressed weights for just-in-time decompression, it significantly reduces data transfer latency. Unlike prior methods focusing solely on weight compression, LEXI also compresses activations and hybrid caches.
Methodology
- �� Utilizes Huffman coding for lossless compression of BF16 exponents.
- �� Compresses activations and caches on-the-fly, stores compressed weights for just-in-time decompression.
- �� Uses multi-lane LUT decoders to ensure maximum link bandwidth.
- �� Design incurs only 0.09% area and energy overhead.
Experiments
Experiments were conducted on Jamba, Zamba, and Qwen models using WikiText-2 and C4 datasets. By simulating communication traffic, LEXI's performance in reducing communication latency and maintaining model accuracy was evaluated. Results show that LEXI reduces inter-chiplet communication latency by 33-45% and end-to-end inference latency by 30-35%.
Results
LEXI reduces inter-chiplet communication latency by 33-45% and end-to-end inference latency by 30-35% on Jamba, Zamba, and Qwen models. Implemented using GF 22 nm technology with only 0.09% area and energy overhead. Achieves lossless compression while maintaining model accuracy on WikiText-2 and C4 datasets.
Applications
LEXI can be directly applied to large language models requiring efficient inter-chiplet communication, especially in multi-chiplet accelerators. Its low area and energy overhead make it suitable for practical deployment of large-scale models.
Limitations & Outlook
In extreme cases, exceeding 32 distinct exponent values may reduce compression efficiency. Additionally, LEXI requires specific hardware support for optimal performance. Future work could explore combining LEXI with other quantization and pruning techniques to further reduce communication overhead.
Plain Language Accessible to non-experts
Imagine you work in a large warehouse, responsible for moving goods from one area to another. Each item has a label (exponent), and you need to move these items as quickly as possible. You notice that there aren't many different types of labels, so you use a special method to compress these labels, reducing the number of trips needed. This is how LEXI works: by compressing the exponent field, it reduces the time data takes to travel between chips, just like moving goods more efficiently in a warehouse.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to transport items between levels. Each item has a label, and you notice these labels are quite similar. So you decide to use a clever way to compress these labels, allowing you to transport items faster. That's how LEXI works: by compressing the exponent part of data, it helps large language models transfer data between chips faster, just like moving items faster in a game.
Glossary
Lossless Compression
A data compression method that ensures the decompressed data is identical to the original data.
Used in LEXI for compressing exponent fields.
Huffman Coding
An algorithm for lossless data compression using variable-length codes to reduce data size.
Used for exponent compression in LEXI.
BF16 Format
A floating-point format using 8-bit exponents and 7-bit mantissas, widely used in neural network training.
LEXI focuses on the exponent field of the BF16 format.
Inter-Chiplet Communication
Data transfer between chips in a multi-chip architecture.
LEXI aims to reduce inter-chiplet communication latency.
Hybrid Cache
A cache structure combining multiple caching strategies to improve data access efficiency.
Used for on-the-fly compression in LEXI.
Open Questions Unanswered questions from this research
- 1 How can LEXI be optimized for different hardware architectures?
- 2 Can LEXI be combined with other compression techniques for greater efficiency?
Applications
Immediate Applications
Large-Scale Model Deployment
LEXI can be used for practical deployment of large language models requiring efficient inter-chiplet communication, especially in multi-chiplet accelerators.
Long-term Vision
Support for Larger Models
By reducing communication latency, LEXI paves the way for supporting even larger large language models.
Abstract
Data movement overheads increase the inference latency of state-of-the-art large language models (LLMs). These models commonly use the bfloat16 (BF16) format for stable training. Floating-point standards allocate eight bits to the exponent, but our profiling reveals that exponent streams exhibit fewer than 3 bits Shannon entropy, indicating high inherent compressibility. To exploit this potential, we propose LEXI, a novel lossless exponent compression scheme based on Huffman coding. LEXI compresses activations and caches on the fly while storing compressed weights for just-in-time decompression near compute, without sacrificing system throughput and model accuracy. The codecs at the ingress and egress ports of network-on-chip routers sustain the maximum link bandwidth via multi-lane LUT decoders, incurring only 0.09 percent area and energy overheads with GF 22 nm technology. LEXI reduces inter-chiplet communication and end-to-end inference latencies by 33-45 percent and 30-35 percent on modern Jamba, Zamba, and Qwen LLMs implemented on a homogeneous chiplet architecture.