Conference proceeding
An Open-Source And Extensible Framework for Fast Prototyping and Benchmarking of Spiking Neural Network Hardware
2024 34th International Conference on Field-Programmable Logic and Applications (FPL), pp 250-256
02 Sep 2024
Abstract
Spiking neural networks (SNNs) are bioplausible machine learning models that use discrete spikes to encode, compute, and transmit information. Combined with event-driven low-power hardware, SNNs can improve the energy efficiency of learning tasks. Although there have been several efforts to build SNN hardware, there is no uniform framework to verify and benchmark these designs in terms of key hardware performance metrics such as inference accuracy, area, power consumption, and throughput. We propose PRONTO, an open-source and extensible framework to verify SNN hardware for different learning tasks and datasets. Given the ubiquity of PyTorch in the machine learning community and for demonstration purposes, the frontend of PRONTO is integrated with a torch-based SNN simulator for model specification and training. Its backend is integrated with an open-source quantized SNN hardware. PRONTO interfaces with a torch code to generate input stimuli which are then driven to SNN hardware through a configurable SystemVerilog testbench, verifying the design across various SNN-specific configurations. PRONTO utilizes a dataflow-based approach to validate SNN models that are segmented and run on a mix of software and hardware platforms. We describe PRONTO and evaluate it using six datasets spanning image, audio, and text classification. We present benchmark results for various input settings. PRONTO is available under an open-source licensing to provide a platform to evaluate all current and future SNN hardware designs. We believe PRONTO will substantially reduce the design verification effort, thus facilitating fast design prototyping.
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Details
- Title
- An Open-Source And Extensible Framework for Fast Prototyping and Benchmarking of Spiking Neural Network Hardware
- Creators
- Shadi Matinizadeh - Drexel UniversityAnup Das - Drexel University
- Publication Details
- 2024 34th International Conference on Field-Programmable Logic and Applications (FPL), pp 250-256
- Publisher
- IEEE; LOS ALAMITOS
- Number of pages
- 7
- Grant note
- Accenture (10.13039/100004672)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; College of Engineering
- Web of Science ID
- WOS:001337952800032
- Scopus ID
- 2-s2.0-85205008781
- Other Identifier
- 991021929987404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods
- Engineering, Electrical & Electronic