High-Performance Data Mapping for BNNs on PCM-based Integrated Photonics
CoRR(2024)
摘要
State-of-the-Art (SotA) hardware implementations of Deep Neural Networks
(DNNs) incur high latencies and costs. Binary Neural Networks (BNNs) are
potential alternative solutions to realize faster implementations without
losing accuracy. In this paper, we first present a new data mapping, called
TacitMap, suited for BNNs implemented based on a Computation-In-Memory (CIM)
architecture. TacitMap maximizes the use of available parallelism, while CIM
architecture eliminates the data movement overhead. We then propose a hardware
accelerator based on optical phase change memory (oPCM) called EinsteinBarrier.
Ein-steinBarrier incorporates TacitMap and adds an extra dimension for
parallelism through wavelength division multiplexing, leading to extra latency
reduction. The simulation results show that, compared to the SotA CIM baseline,
TacitMap and EinsteinBarrier significantly improve execution time by up to
154x and 3113x, respectively, while also maintaining the energy consumption
within 60
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