H3PIMAP: A Heterogeneity-Aware Multi-Objective DNN Mapping Framework on Electronic-Photonic Processing-in-Memory Architectures
Journal:
arXiv
Published Date:
Mar 10, 2025
Abstract
The future of artificial intelligence (AI) acceleration demands a paradigm
shift beyond the limitations of purely electronic or photonic architectures.
Photonic analog computing delivers unmatched speed and parallelism but
struggles with data movement, robustness, and precision. Electronic
processing-in-memory (PIM) enables energy-efficient computing by co-locating
storage and computation but suffers from endurance and reconfiguration
constraints, limiting it to static weight mapping. Neither approach alone
achieves the balance needed for adaptive, efficient AI. To break this impasse,
we study a hybrid electronic-photonic-PIM computing architecture and introduce
H3PIMAP, a heterogeneity-aware mapping framework that seamlessly orchestrates
workloads across electronic and optical tiers. By optimizing workload
partitioning through a two-stage multi-objective exploration method, H3PIMAP
harnesses light speed for high-throughput operations and PIM efficiency for
memory-bound tasks. System-level evaluations on language and vision models show
H3PIMAP achieves a 2.74x energy efficiency improvement and a 3.47x latency
reduction compared to homogeneous architectures and naive mapping strategies.
This proposed framework lays the foundation for hybrid AI accelerators,
bridging the gap between electronic and photonic computation for
next-generation efficiency and scalability.
