GaBAN: A Generic and Flexibly Programmable Vector Neuro-processor on FPGA

Spiking neural network (SNN) is the main computational model of brain-inspired computing and neuroscience, which also acts as the bridge between them. With the rapid development of neuroscience, accurate and flexible SNN simulation with high performance is becoming important. This paper proposes GaBAN, a generic and flexibly programmable neuro-processor on FPGA. Different from the majority of current designs that realize neural components by custom hardware directly, it is centered on a compact, versatile vector instruction set, which supports multiple-precision vector calculation, indexed-/strided-memory access, and conditional execution to accommodate computational characteristics. By software and hardware co-design, the compiler extracts memory-accesses from SNN programs to generate micro-ops executed by an independent hardware unit; the latter interacts with the computing pipeline through an asynchronous buffering mechanism. Thus memory access delay can fully cover the calculation. Tests show that GaBAN can not only outperform the SOTA ISA-based FPGA solution remarkably but also be comparable with counterparts of the hardware-fixed model on some tasks. Moreover, in end-to-end testing, its simulation performance exceeds that of high-performance X86 processor (1.44-3.0x).