提出自动设计光子Transformer加速器的方法，兼顾性能与能效约束。
DxPTA: An Architecture Design Space Exploration with Optical Dataflow-guided Strategy for HW/SW Co-Design of Photonic Transformer Accelerators
- 基于相干光数据流分析确定架构参数，实现约束感知搜索。
- 在50mm²、5W等限制下，达成26mm²面积、4.8W功耗、39mJ能耗、6ms延迟。
- 搜索速度比穷举法快15.2倍，适合AGI应用的高效硬件协同设计。
基于Transformer的模型已成为性能领先的AI模型，有望推动通用人工智能（AGI）发展。然而其庞大的规模仍阻碍高效实现，亟需替代方案以实现节能加速。近期研究提出了光子Transformer加速器（PTA），相较传统电子加速器显著提升速度与能效。但现有PTA架构未考虑应用约束（如面积、功耗、能耗、延迟），且依赖人工设计，耗时巨大，难以扩展。为此，本文提出DxPTA，一种新型设计空间探索方法，支持满足多约束的软硬件协同设计。该方法通过：(1) 基于相干光数据流识别PTA架构参数；(2) 分析参数影响；(3) 构建约束感知搜索算法。实验表明，DxPTA可为DeiT-T/S/B和BERT-B/L等模型找到合适架构，在50mm²面积、5W功耗、50mJ能耗、10ms延迟约束下，实现26mm²面积、4.8W功耗、39mJ能耗、6ms延迟，搜索时间较穷举法快15.2倍，验证了其在多样化AGI应用中的高效设计潜力。
原文摘要 · Abstract (English)
Transformer-based networks have emerged as prominent AI models with state-of-the-art performance, which potentially pave the way toward artificial general intelligence (AGI). However, their large sizes still hinder their efficient implementation, thus highlighting the need for alternate solutions to enable their energy-efficient acceleration. Recently, state-of-the-art works propose photonic transformer accelerators (PTAs) with significant speedup and energy efficiency improvements over the conventional electronic accelerators. However, their PTA architectures are developed without considering the application constraints (e.g., area, power, energy, and latency). Moreover, their manual design approach also requires huge design time to determine a suitable architecture for the targeted application, hence making this approach not scalable. To address these limitations, we propose DxPTA, a novel design space exploration methodology for enabling efficient hardware/software co-design of the appropriate PTA architecture that meets all constraints. It is achieved by (1) identifying the PTA architecture parameters based on the coherent optical dataflow; (2) analyzing the impact/significance of the parameters; and (3) leveraging this analysis for devising a constraint-aware architecture search algorithm. Experimental results show that, our DxPTA can find the appropriate PTA architectures for different transformer-based models (i.e., DeiT-T/S/B and BERT-B/L). It achieves up to 26mm^2 area, 4.8W power, 39mJ energy, and 6ms latency, for constraints of 50mm^2 area, 5W power, 50mJ energy, and 10ms latency; with 15.2x faster searching time than the exhaustive approach. These results demonstrate the potential of DxPTA methodology for enabling efficient PTA designs for diverse AGI-based applications.
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