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.
An open-source end-to-end multi-agent LLM workflow that takes user specs as input and outputs the appropriate netlist, encompassing both topology generation and circuit sizing, and achieves a figure of merit (FoM) comparable to that of known topologies, and up to 3x higher for certain circuits.
Edge inference on resource-constrained embedded nodes demands accelerators that are energy-efficient and compact. This paper presents Versat-AI, an open-source compiler that accepts a standard Open Neural Network Exchange (ONNX) model and generates a complete, synthesisable RISC-V System-on-Chip (SoC) with an embedded...
R. Teixeira, J. Rodrigues, Jaime Aguiar et al.· Journal of Low Power Electro...· 0 citations
Findings indicate that the ACORISCVbSNN model has the potential to advance the field of bio-inspired computing, providing a highly accurate, energyefficient, and low-latency system for real-world use.
Yamini Devi Ykuntam, M. V. Nageswara Rao, Leela Kumari. B.· International Journal of Com...· 0 citations
The view taken here is that brain-inspired computing is heading toward a hybrid future: conventional digital processors will keep doing what they do best, while event-driven and in-memory accelerators take over the workloads where they have a genuine edge.
Jisna C. Jeejo, Habeeba M. A.· International Journal of Tec...· 0 citations
A novel open-source framework named OSCAR is proposed, which, given a set of hardware and workload specifications, provides architecture-level power estimation and can also automatically generate Chisel and synthesizable RTL of the custom AI chip.
J. Mok, Qi-Jun Zhang, Di Pang et al.· ACM Transactions on Design A...· 0 citations
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