Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview
I Aliyev, K Svoboda, T Adegbija… - 2024 IEEE 17th …, 2024 - ieeexplore.ieee.org
Spiking Neural Networks (SNNs) are inspired by the sparse and event-driven nature of
biological neural processing, and offer the potential for ultra-low-power artificial intelligence …
biological neural processing, and offer the potential for ultra-low-power artificial intelligence …
PULSE: Parametric Hardware Units for Low-power Sparsity-Aware Convolution Engine
I Aliyev, T Adegbija - arxiv preprint arxiv:2402.06210, 2024 - arxiv.org
Spiking Neural Networks (SNNs) have become popular for their more bio-realistic behavior
than Artificial Neural Networks (ANNs). However, effectively leveraging the intrinsic …
than Artificial Neural Networks (ANNs). However, effectively leveraging the intrinsic …
Exploring the Sparsity-Quantization Interplay on a Novel Hybrid SNN Event-Driven Architecture
I Aliyev, J Lopez, T Adegbija - arxiv preprint arxiv:2411.15409, 2024 - arxiv.org
Spiking Neural Networks (SNNs) offer potential advantages in energy efficiency but currently
trail Artificial Neural Networks (ANNs) in versatility, largely due to challenges in efficient …
trail Artificial Neural Networks (ANNs) in versatility, largely due to challenges in efficient …
Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Network Accelerators
I Aliyev - 2024 - search.proquest.com
Abstract Spiking Neural Networks (SNNs) are bio-inspired event-driven alternatives to
Artificial Neural Networks (ANNs), offering the potential for energy-efficient artificial …
Artificial Neural Networks (ANNs), offering the potential for energy-efficient artificial …