DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware
Shihao Song, Harry Chong, Adarsha Balaji, Anup Das, James Shackleford,, Nagarajan Kandasamy

TL;DR
DFSynthesizer is an end-to-end framework that synthesizes spiking neural networks onto neuromorphic hardware, optimizing resource utilization and performance guarantees through workload analysis, clustering, and scheduling.
Contribution
It introduces a novel, comprehensive approach for mapping SNNs onto neuromorphic hardware, addressing resource and latency constraints effectively.
Findings
Provides tighter performance guarantees than existing methods
Successfully maps 10 machine-learning programs onto neuromorphic hardware
Improves hardware utilization and throughput
Abstract
Spiking Neural Networks (SNN) are an emerging computation model, which uses event-driven activation and bio-inspired learning algorithms. SNN-based machine-learning programs are typically executed on tile- based neuromorphic hardware platforms, where each tile consists of a computation unit called crossbar, which maps neurons and synapses of the program. However, synthesizing such programs on an off-the-shelf neuromorphic hardware is challenging. This is because of the inherent resource and latency limitations of the hardware, which impact both model performance, e.g., accuracy, and hardware performance, e.g., throughput. We propose DFSynthesizer, an end-to-end framework for synthesizing SNN-based machine learning programs to neuromorphic hardware. The proposed framework works in four steps. First, it analyzes a machine-learning program and generates SNN workload using representative…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices · Neural dynamics and brain function
