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DTSTAMP:20260402T024534Z
LOCATION:3002\, Level 3
DTSTART;TZID=America/Los_Angeles:20250623T104500
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UID:dac_DAC 2025_sess120_RESEARCH1293@linklings.com
SUMMARY:P-DAC: Power-Efficient Photonic Accelerators for LLM Inference
DESCRIPTION:Wen-Tse Chang and Chun-Feng Wu (National Yang Ming Chiao Tung 
 University) and Yun-Chen Lo (Harvard University)\n\nAs traditional electro
 nic hardware encounters the limitations of Moore's Law, optical computing 
 is emerging as a promising alternative, delivering high data transmission 
 rates, especially beneficial for big data and AI applications. Photonic ac
 celerators, such as the Lightening-Transformer, utilize optical analog sig
 nals to accelerate Transformer-based models, achieving exceptional speed a
 nd low energy consumption. However, controlling modern optical intensity m
 odulators (e.g., Mach-Zehnder Modulators) requires using electrical analog
  signals (e.g., voltage values) to adjust the optical signal intensity for
  realizing optical-based vector inner product calculations. Managing this 
 modulation consumes significant power, as it involves selecting optimal el
 ectrical values through an electrical controller and converting digital si
 gnals to analog using digital-to-analog converters (DACs). In this work, w
 e introduce P-DAC, a solution designed to reduce DAC power consumption, si
 gnificantly enhancing the energy efficiency of optical accelerators for Tr
 ansformer models.\n\nTopics: Design\n\nTracks: DES2A: In-memory and Near-m
 emory Computing Circuits\n\nSession Chairs: Yu Cao (University of Minnesot
 a) and Sumitha George (North Dakota State University)\n\n
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