  With the recent advances in optical phase change material (PCM), photonic
in-memory neurocomputing has demonstrated its superiority in optical neural
network (ONN) designs with near-zero static power consumption, time-of-light
latency, and compact footprint. However, photonic tensor cores require massive
hardware reuse to implement large matrix multiplication due to the limited
single-core scale. The resultant large number of PCM writes leads to serious
dynamic power and overwhelms the fragile PCM with limited write endurance. In
this work, we propose a synergistic optimization framework, ELight, to minimize
the overall write efforts for efficient and reliable optical in-memory
neurocomputing. We first propose write-aware training to encourage the
similarity among weight blocks, and combine it with a post-training
optimization method to reduce programming efforts by eliminating redundant
writes. Experiments show that ELight can achieve over 20X reduction in the
total number of writes and dynamic power with comparable accuracy. With our
ELight, photonic in-memory neurocomputing will step forward towards viable
applications in machine learning with preserved accuracy, order-of-magnitude
longer lifetime, and lower programming energy.
