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This paper introduces Syn2Logic, a novel electronic Neuromorphic Design Automation (eNDA) framework that integrates computational neuroscience with traditional Electronic Design Automation (EDA). By employing a custom domain-specific language (DSL) and a compiler, Syn2Logic enables neuroscientists to model neural behavior and generate synthesizable RTL hardware without writing HDL code. The framework achieves significant advancements, including the fastest C. elegans accelerator, a highly efficient neuromorphic sudoku solver, and an energy-efficient accelerator for the MNIST dataset, all outperforming existing solutions in speed and efficiency.
Syn2Logic generates state-of-the-art neuromorphic hardware without a single line of HDL code, revolutionizing how neuroscientists can implement their models.
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.