Conference proceeding
QUANTISENC++: A Fully-Configurable Many-Core Neuromorphic Hardware
Conference record - Asilomar Conference on Signals, Systems, & Computers, pp 1527-1531
04 Apr 2025
Abstract
Spiking neural networks (SNNs) use discrete spikes to process information, offering a more biologically realistic computation model compared to artificial neural networks (ANNs). We propose QUANTISENC++, a layer-based neuromorphic design to map and execute SNN models with different network topologies. A layer in QUANTISENC++ integrates leaky integrate and fire (LIF) neurons with a dedicated synaptic memory. Layers can operate independently of each other. QUANTISENC++ supports fixed-point arithmetic operations with variable quantization and mixed precision. It uses approximate adders to reduce the area and power overheads and optimize the performance of SNNs. In addition to configuring QUANTISENC++ as mono-lithic hardware, we propose a many-core architecture using a shared interconnect. We introduce a compiler that uses a graph clustering algorithm to analyze a given SNN model and map it to the many-core versions of QUANTISENC++. We evaluate QUANTISENC++ on different datasets, comparing monolithic and many-core configurations with state-of-the-art designs. Our results show a significant improvement in performance, area, and power efficiency, highlighting QUANTISENC++ as an effective solution for SNN hardware implementations.
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Details
- Title
- QUANTISENC++: A Fully-Configurable Many-Core Neuromorphic Hardware
- Creators
- Shadi Matinizadeh - Drexel UniversityArghavan Mohammadhassani - Drexel UniversityM. L Varshika - Drexel UniversitySarah Johari - Drexel UniversityNagarajan Kandasamy - Drexel UniversityAnup Das - Drexel University
- Publication Details
- Conference record - Asilomar Conference on Signals, Systems, & Computers, pp 1527-1531
- Conference
- Asilomar Conference on Signals, Systems, and Computers, 58th (Pacific Grove, CA, USA, 27 Oct 2024–30 Oct 2024)
- Series
- Conference Record of the Asilomar Conference on Signals Systems and Computers
- Publisher
- IEEE
- Number of pages
- 5
- Grant note
- Accenture LLPDOE: DE-SC0022014 NSF: OAC-2209745, CCF-1942697
This work is supported by Accenture LLP, the DOE DE-SC0022014, and NSF OAC-2209745 & CCF-1942697.
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; Computer Science (Computing); College of Engineering
- Web of Science ID
- WOS:001479671800280
- Scopus ID
- 2-s2.0-105002678558
- Other Identifier
- 991022047146604721