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This paper introduces Syn2Logic, an end-to-end framework for electronic Neuromorphic Design Automation (eNDA) that integrates computational neuroscience modeling with traditional Electronic Design Automation (EDA). By utilizing a custom domain-specific language (DSL) and a compiler, Syn2Logic enables neuroscientists to model neural behaviors and generate synthesizable RTL hardware without writing HDL code. The framework demonstrates significant advancements, including the fastest C. elegans accelerator, a superior neuromorphic sudoku solver, and an energy-efficient FPGA accelerator that outperforms existing architectures on the MNIST dataset.
Syn2Logic achieves groundbreaking speed and efficiency in neuromorphic computing without requiring any hardware description language coding.
In this work, we propose a view on electronic Neuromorphic Design Automation (eNDA), which we see as a design automation flow that bridges computational neuroscience modeling with traditional Electronic Design Automation (EDA) flow. We introduce the term, give examples of how it can be implemented, and design a prototype implementation: Syn2Logic. Syn2Logic is an entire eNDA framework, that allows neuroscientists to model neural behavior using a custom DSL and a compiler that takes the same model description down to synthesizable RTL hardware. We end the paper by applying the eNDA-flow through Syn2Logic to show how to -- without writing a single line of hardware description language (HDL) code-- (i) generate what we believe is the fastest C. elegans accelerator that runs significantly faster than state-of-the-art simulators, (ii) create (to the best of our knowledge) the fastest, most generic neuromorphic sudoku solver that outperforms CP-SAT and SCIP on TOP1465 puzzles, and (iii) create a 5.6 million FPS/Watt accelerator on a tiny FPGA that outperforms existing neuromorphic architectures in terms of speed and energy-efficiency on the MNIST dataset.
