5 papers · 1 filter
Surrogates, Spikes, and Sparsity: Performance Analysis and Characterization of SNN Hyperparameters on Hardware
Ilkin Aliyev, Jesus Lopez, Tosiron Adegbija
Spiking Neural Networks (SNNs) offer inherent advantages for low-power inference through sparse, event-driven computation. However, the theoretical energy benefits of SNNs are ofte…
Exploring the Sparsity-Quantization Interplay on a Novel Hybrid SNN Event-Driven Architecture
Ilkin Aliyev, Jesus Lopez, Tosiron Adegbija
Spiking Neural Networks (SNNs) offer potential advantages in energy efficiency but currently trail Artificial Neural Networks (ANNs) in versatility, largely due to challenges in ef…
Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview
Ilkin Aliyev, Kama Svoboda, Tosiron Adegbija +1
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 intelligen…
PULSE: Parametric Hardware Units for Low-power Sparsity-Aware Convolution Engine
Ilkin Aliyev, Tosiron Adegbija
Spiking Neural Networks (SNNs) have become popular for their more bio-realistic behavior than Artificial Neural Networks (ANNs). However, effectively leveraging the intrinsic, unst…
Design Space Exploration of Sparsity-Aware Application-Specific Spiking Neural Network Accelerators
Ilkin Aliyev. Kama Svoboda, Tosiron Adegbija
Spiking Neural Networks (SNNs) offer a promising alternative to Artificial Neural Networks (ANNs) for deep learning applications, particularly in resource-constrained systems. This…