2022/08/17 by Hanqing Zhu, Keren Zhu, Zhu, Hanqing +11 · 1 citation
Computer Science · Engineering · #Advanced Memory and Neural Computing #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Neural Networks and Reservoir Computing #Photonic and Optical Devices
paper · pdf · doi:10.48550/arxiv.2208.08099
openalex publication_date 2022/08/17 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Analog computing has been recognized as a promising low-power alternative to digital counterparts for neural network acceleration. However, conventional analog computing is mainly in a mixed-signal manner. Tedious analog/digital (A/D) conversion cost significantly limits the overall system's energy efficiency. In this work, we devise an efficient analog activation unit with magnetic tunnel junction (MTJ)-based analog content-addressable memory (MACAM), simultaneously realizing nonlinear activation and A/D conversion in a fused fashion. To compensate for the nascent and therefore currently limited representation capability of MACAM, we propose to mix our analog activation unit with digital activation dataflow. A fully differential framework, SuperMixer, is developed to search for an optimized activation workload assignment, adaptive to various activation energy constraints. The effectiveness of our proposed methods is evaluated on a silicon photonic accelerator. Compared to standard activation implementation, our mixed activation system with the searched assignment can achieve competitive accuracy with >60% energy saving on A/D conversion and activation.