transformer accelerator

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ASAP: A 28-nm Transformer Training Accelerator With Alternating Sparsity and Asymmetrical Microscaling Precision

ASAP: A 28-nm Transformer Training Accelerator With Alternating Sparsity and Asymmetrical Microscaling Precision 150 150
Author(s): HanGyeol Mun, Jian Meng, Xiaofeng Hu, Yuan Liao, Chun-Ting Chen, Jae-sun Seo

Abstract:

This work presents ASAP, a 28-nm transformer-training accelerator that combines N:M structured sparsity with asymmetric microscaling floating-point (MXFP) precision through a unified algorithm–hardware co-design. ASAP introduces a progressive sparsity schedule in which pruned compute resources are reassigned to increase numerical precision for important weights and activations, stabilizing optimization …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 14
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3693653
Publisher: IEEE

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A BEV Perception Transformer Accelerator With Saliency-Driven Image/Point Cloud Fusion and Phase-Linked Dataflow in 28 nm CMOS

A BEV Perception Transformer Accelerator With Saliency-Driven Image/Point Cloud Fusion and Phase-Linked Dataflow in 28 nm CMOS 150 150
Author(s): Junyi Qian, Yuncheng Lu, Zhangrui Qian, Lishuo Deng, Zhihao Yan, Cai Li, Tony Tae-Hyoung Kim, Weiwei Shan

Abstract:

Deploying advanced Transformer-based models for real-time, high-accuracy multimodal bird’s-eye-view (BEV) perception in autonomous driving imposes substantial hardware demands. To address this, we propose a low-cost, low-power image/point-cloud fusion Transformer accelerator that supports two modes: high-performance driving and ultra-low-power sentry operation. We first propose a cross-modal saliency evaluation mechanism …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 12
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3691770
Publisher: IEEE

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