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UID:dac_DAC 2026_sess315_RESEARCH2094@linklings.com
SUMMARY:DAPA: Distribution Aware Piecewise Activation Functions for On-Dev
 ice Transformer Inference and Training
DESCRIPTION:Maoyang Xiang and Bo Wang (Singapore University of Technology 
 and Design)\n\nNon-linear activation functions play a pivotal role in on-d
 evice inference and training, as they not only consume substantial hardwar
 e resources but also impose a significant impact on system performance and
  energy efficiency.\nIn this work, we propose Distribution-Aware Piecewise
  Activation (DAPA), a differentiable and hardware-friendly activation func
 tion for Transformer architectures by exploiting the distribution of pre-a
 ctivation data. DAPA employs a non-uniform piecewise approximation that al
 locates finer segments to high-probability regions of the distribution, im
 proving generalizability over prior piecewise linear methods. The resultin
 g approximation is further quantized using Distribution-Weighted Mean Squa
 re Error to reduce latency and resource utilization for hardware deploymen
 t.\nOur HLS implementation demonstrates that DAPA speeds up GELU computati
 on by 16x and decreases DSP utilization by 16x while maintaining comparabl
 e or better performance across vision Transformers and GPT-2 models.\n\nTo
 pics: Quantum\n\nTrack: DES6. Quantum Computing, Systems\n\nSession Chairs
 : Hokeun Kim (Arizona State University); BaekGyu Kim (Daegu Gyeongbuk Inst
 itute of Science and Technology); Xiang Chen (Peking University); Sangyeob
  Kim (Yonsei University); Robert M. Radway (University of Pennsylvania); P
 rashanth Mohan (University of Pittsburgh); Xiaoxuan Yang (University of Vi
 rginia, Stanford University); Ramtin Zand (University of South Carolina); 
 Hadi Kamali (University of Central Florida); Jiafeng Xie (Villanova Univer
 sity); Prabuddha Chakraborty (University of Maine); Junichiro Kadomoto (Un
 iversity of Tokyo); and Yosuke Ueno (RIKEN)\n\n
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