Reshape and Adapt for Output Quantization (RAOQ): Quantization-aware Training for In-memory Computing Systems

Bonan Zhang, Chia-Yu Chen, Naveen Verma
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:58739-58762, 2024.

Abstract

In-memory computing (IMC) has emerged as a promising solution to address both computation and data-movement challenges, by performing computation on data in-place directly in the memory array. IMC typically relies on analog operation, which makes analog-to-digital converters (ADCs) necessary, for converting results back to the digital domain. However, ADCs maintain computational efficiency by having limited precision, leading to substantial quantization errors in compute outputs. This work proposes RAOQ (Reshape and Adapt for Output Quantization) to overcome this issue, which comprises two classes of mechanisms including: 1) mitigating ADC quantization error by adjusting the statistics of activations and weights, through an activation-shifting approach (A-shift) and a weight reshaping technique (W-reshape); 2) adapting AI models to better tolerate ADC quantization through a bit augmentation method (BitAug), complemented by the introduction of ADC-LoRA, a low-rank approximation technique, to reduce the training overhead. RAOQ demonstrates consistently high performance across different scales and domains of neural network models for computer vision and natural language processing (NLP) tasks at various bit precisions, achieving state-of-the-art results with practical IMC implementations.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-zhang24i, title = {Reshape and Adapt for Output Quantization ({RAOQ}): Quantization-aware Training for In-memory Computing Systems}, author = {Zhang, Bonan and Chen, Chia-Yu and Verma, Naveen}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {58739--58762}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/zhang24i/zhang24i.pdf}, url = {https://proceedings.mlr.press/v235/zhang24i.html}, abstract = {In-memory computing (IMC) has emerged as a promising solution to address both computation and data-movement challenges, by performing computation on data in-place directly in the memory array. IMC typically relies on analog operation, which makes analog-to-digital converters (ADCs) necessary, for converting results back to the digital domain. However, ADCs maintain computational efficiency by having limited precision, leading to substantial quantization errors in compute outputs. This work proposes RAOQ (Reshape and Adapt for Output Quantization) to overcome this issue, which comprises two classes of mechanisms including: 1) mitigating ADC quantization error by adjusting the statistics of activations and weights, through an activation-shifting approach (A-shift) and a weight reshaping technique (W-reshape); 2) adapting AI models to better tolerate ADC quantization through a bit augmentation method (BitAug), complemented by the introduction of ADC-LoRA, a low-rank approximation technique, to reduce the training overhead. RAOQ demonstrates consistently high performance across different scales and domains of neural network models for computer vision and natural language processing (NLP) tasks at various bit precisions, achieving state-of-the-art results with practical IMC implementations.} }
Endnote
%0 Conference Paper %T Reshape and Adapt for Output Quantization (RAOQ): Quantization-aware Training for In-memory Computing Systems %A Bonan Zhang %A Chia-Yu Chen %A Naveen Verma %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-zhang24i %I PMLR %P 58739--58762 %U https://proceedings.mlr.press/v235/zhang24i.html %V 235 %X In-memory computing (IMC) has emerged as a promising solution to address both computation and data-movement challenges, by performing computation on data in-place directly in the memory array. IMC typically relies on analog operation, which makes analog-to-digital converters (ADCs) necessary, for converting results back to the digital domain. However, ADCs maintain computational efficiency by having limited precision, leading to substantial quantization errors in compute outputs. This work proposes RAOQ (Reshape and Adapt for Output Quantization) to overcome this issue, which comprises two classes of mechanisms including: 1) mitigating ADC quantization error by adjusting the statistics of activations and weights, through an activation-shifting approach (A-shift) and a weight reshaping technique (W-reshape); 2) adapting AI models to better tolerate ADC quantization through a bit augmentation method (BitAug), complemented by the introduction of ADC-LoRA, a low-rank approximation technique, to reduce the training overhead. RAOQ demonstrates consistently high performance across different scales and domains of neural network models for computer vision and natural language processing (NLP) tasks at various bit precisions, achieving state-of-the-art results with practical IMC implementations.
APA
Zhang, B., Chen, C. & Verma, N.. (2024). Reshape and Adapt for Output Quantization (RAOQ): Quantization-aware Training for In-memory Computing Systems. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:58739-58762 Available from https://proceedings.mlr.press/v235/zhang24i.html.

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