DSL-based SNN accelerator design using Chisel

Neural Networks (NNs) are a very active field of research that also has wide-ranging applications in industry. An emerging type of NN that is promising for hardware acceleration and low energy requirements are Spiking Neural Networks (SNNs). But design automation in terms of accelerator circuit generation is still lacking proper search techniques for optimization of network parameters including the selection of proper neuron models and spike encodings. They are often restricted to implement a single network setting and/or a fixed hardware architecture.

In this paper, we present a novel multi-layer Domain-Specific Language (DSL) for constructing sequential circuits, including building blocks for pipelines supporting hazard detection. As the host language, we use Chisel, a hardware construction language allowing to express hardware at Register-Transfer Level and above. In contrast to applying High-Level Synthesis, we introduce a domain-specific language (DSL) for SNN accelerator design based on Chisel by defining building blocks for SNNs. After introducing this DSL, we present a full SNN accelerator generation framework that covers all phases, from training to deployment. Also proposed is a design space exploration for various SNN accelerator designs using different neuron models, their parametrizations as well as spike encodings. The generated designs are evaluated in terms of execution time, power consumption, classification accuracy, and resource usage when mapped to Field-Programmable Gate Arrays (FPGAs) for the MNIST, Fashion-MNIST, SVHN, and CIFAR-10 data sets.