BitSAD v2: Compiler Optimization and Analysis for Bitstream Computing

Computer vision and machine learning algorithms operating under a strict power budget require an alternate computing paradigm. While bitstream computing (BC) satisfies these constraints, creating BC systems is difficult. To address the design challenges, we propose compiler extensions to BitSAD, a DSL for BC. Our work enables bit-level software emulation and automated generation of hierarchical hardware, discusses potential optimizations, and proposes compiler phases to implement those optimizations in a hardware-aware manner. Finally, we introduce population coding, a parallelization scheme for stochastic computing that decreases latency without sacrificing accuracy, and provide theoretical and experimental guarantees on its effectiveness.