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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization

Seunghee Han, Soongyu Choi, Joo-Young Kim

TL;DR

LightNobel tackles the sequence-length scalability bottleneck in Protein Structure Prediction Models by introducing Token-wise Adaptive Activation Quantization (AAQ) and a hardware-software co-designed accelerator. The approach exploits token-level activation patterns to apply dynamic precision and outlier handling, while the LightNobel architecture (RMPU and VVPU) enables efficient multi-precision, token-wise attention processing. Empirical results show major improvements in speed and power efficiency over state-of-the-art GPUs, along with dramatic memory reductions, enabling longer protein sequences to be processed. This work offers a practical pathway to scalable, high-accuracy PPM inference with specialized hardware.

Abstract

Recent advances in Protein Structure Prediction Models (PPMs), such as AlphaFold2 and ESMFold, have revolutionized computational biology by achieving unprecedented accuracy in predicting three-dimensional protein folding structures. However, these models face significant scalability challenges, particularly when processing proteins with long amino acid sequences (e.g., sequence length > 1,000). The primary bottleneck that arises from the exponential growth in activation sizes is driven by the unique data structure in PPM, which introduces an additional dimension that leads to substantial memory and computational demands. These limitations have hindered the effective scaling of PPM for real-world applications, such as analyzing large proteins or complex multimers with critical biological and pharmaceutical relevance. In this paper, we present LightNobel, the first hardware-software co-designed accelerator developed to overcome scalability limitations on the sequence length in PPM. At the software level, we propose Token-wise Adaptive Activation Quantization (AAQ), which leverages unique token-wise characteristics, such as distogram patterns in PPM activations, to enable fine-grained quantization techniques without compromising accuracy. At the hardware level, LightNobel integrates the multi-precision reconfigurable matrix processing unit (RMPU) and versatile vector processing unit (VVPU) to enable the efficient execution of AAQ. Through these innovations, LightNobel achieves up to 8.44x, 8.41x speedup and 37.29x, 43.35x higher power efficiency over the latest NVIDIA A100 and H100 GPUs, respectively, while maintaining negligible accuracy loss. It also reduces the peak memory requirement up to 120.05x in PPM, enabling scalable processing for proteins with long sequences.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization

TL;DR

LightNobel tackles the sequence-length scalability bottleneck in Protein Structure Prediction Models by introducing Token-wise Adaptive Activation Quantization (AAQ) and a hardware-software co-designed accelerator. The approach exploits token-level activation patterns to apply dynamic precision and outlier handling, while the LightNobel architecture (RMPU and VVPU) enables efficient multi-precision, token-wise attention processing. Empirical results show major improvements in speed and power efficiency over state-of-the-art GPUs, along with dramatic memory reductions, enabling longer protein sequences to be processed. This work offers a practical pathway to scalable, high-accuracy PPM inference with specialized hardware.

Abstract

Recent advances in Protein Structure Prediction Models (PPMs), such as AlphaFold2 and ESMFold, have revolutionized computational biology by achieving unprecedented accuracy in predicting three-dimensional protein folding structures. However, these models face significant scalability challenges, particularly when processing proteins with long amino acid sequences (e.g., sequence length > 1,000). The primary bottleneck that arises from the exponential growth in activation sizes is driven by the unique data structure in PPM, which introduces an additional dimension that leads to substantial memory and computational demands. These limitations have hindered the effective scaling of PPM for real-world applications, such as analyzing large proteins or complex multimers with critical biological and pharmaceutical relevance. In this paper, we present LightNobel, the first hardware-software co-designed accelerator developed to overcome scalability limitations on the sequence length in PPM. At the software level, we propose Token-wise Adaptive Activation Quantization (AAQ), which leverages unique token-wise characteristics, such as distogram patterns in PPM activations, to enable fine-grained quantization techniques without compromising accuracy. At the hardware level, LightNobel integrates the multi-precision reconfigurable matrix processing unit (RMPU) and versatile vector processing unit (VVPU) to enable the efficient execution of AAQ. Through these innovations, LightNobel achieves up to 8.44x, 8.41x speedup and 37.29x, 43.35x higher power efficiency over the latest NVIDIA A100 and H100 GPUs, respectively, while maintaining negligible accuracy loss. It also reduces the peak memory requirement up to 120.05x in PPM, enabling scalable processing for proteins with long sequences.
Paper Structure (34 sections, 1 equation, 16 figures, 2 tables)

This paper contains 34 sections, 1 equation, 16 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Attention-based Model
  4. Model Quantization
  5. Protein Structure Prediction Model (PPM)
  6. TM-Score
  7. Motivation
  8. PPM Latency Analysis
  9. Activation Size Explosion
  10. Token-Wise Distogram Pattern in Activation
  11. Characteristic Difference Across Activations
  12. Token-wise Adaptive Activation Quantization
  13. Baseline Token-wise Quantization
  14. Activation Adaptive Quantization
  15. Memory Layout for Quantization
  16. ...and 19 more sections

Figures (16)

  • Figure 1: Visualization of Protein Structure Prediction Model (PPM) results. (a) Result of the conventional PPM. (b) Result of LightNobel. (c) Comparison between the two results.
  • Figure 2: Overview of PPM. (a) Block diagram of the PPM. (b) Dataflow of Protein Folding Block (1 Block).
  • Figure 3: Latency Breakdown of PPM with (a) protein R0271 (77 amino acids) and (b) protein T1269 (1,410 amino acids).
  • Figure 4: Analysis of total weight size and peak activation size in PPM across various sequence lengths.
  • Figure 5: Analysis of activation value distribution in PPM. Visualization of (a) three representative channels and (b) three representative tokens.
  • ...and 11 more figures