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Preprint
A Fully-Configurable Open-Source Software-Defined Digital Quantized Spiking Neural Core Architecture
arXiv.org
02 Apr 2024
Abstract
We introduce QUANTISENC, a fully configurable open-source software-defined
digital quantized spiking neural core architecture to advance research in
neuromorphic computing. QUANTISENC is designed hierarchically using a bottom-up
methodology with multiple neurons in each layer and multiple layers in each
core. The number of layers and neurons per layer can be configured via software
in a top-down methodology to generate the hardware for a target spiking neural
network (SNN) model. QUANTISENC uses leaky integrate and fire neurons (LIF) and
current-based excitatory and inhibitory synapses (CUBA). The nonlinear dynamics
of a neuron can be configured at run-time via programming its internal control
registers. Each neuron performs signed fixed-point arithmetic with user-defined
quantization and decimal precision. QUANTISENC supports all-to-all, one-to-one,
and Gaussian connections between layers. Its hardware-software interface is
integrated with a PyTorch-based SNN simulator. This integration allows to
define and train an SNN model in PyTorch and evaluate the hardware performance
(e.g., area, power, latency, and throughput) through FPGA prototyping and ASIC
design. The hardware-software interface also takes advantage of the layer-based
architecture and distributed memory organization of QUANTISENC to enable
pipelining by overlapping computations on streaming data. Overall, the proposed
software-defined hardware design methodology offers flexibility similar to that
of high-level synthesis (HLS), but provides better hardware performance with
zero hardware development effort. We evaluate QUANTISENC using three spiking
datasets and show its superior performance against state-of the-art designs.
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Details
- Title
- A Fully-Configurable Open-Source Software-Defined Digital Quantized Spiking Neural Core Architecture
- Creators
- Shadi MatinizadehNoah Pacik-NelsonIoannis PolykretisKrupa TishbiSuman KumarM. L VarshikaArghavan MohammadhassaniAbhishek MishraNagarajan KandasamyJames ShacklefordEric GalloAnup Das
- Publication Details
- arXiv.org
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Electrical and Computer Engineering; Computer Science; College of Engineering
- Other Identifier
- 991021864960804721