5 papers · 1 filter
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…
GPU-RANC: A CUDA Accelerated Simulation Framework for Neuromorphic Architectures
Sahil Hassan, Michael Inouye, Miguel C. Gonzalez +4
Open-source simulation tools play a crucial role for neuromorphic application engineers and hardware architects to investigate performance bottlenecks and explore design optimizati…
Fine-Tuning Surrogate Gradient Learning for Optimal Hardware Performance in Spiking Neural Networks
Ilkin Aliyev, Tosiron Adegbija
The highly sparse activations in Spiking Neural Networks (SNNs) can provide tremendous energy efficiency benefits when carefully exploited in hardware. The behavior of sparsity in…
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…