8 papers
Mega: A 22 nm Convolutional Spiking Neural Network Accelerator Achieving 0.375 pJ/SOP for Efficient Edge Vision
Rick Luiken, Manil Dev Gomony, Sander Stuijk
Convolutional Spiking Neural Networks (SNN) offer the potential for highly energy-efficient vision processing by exploiting sparse, event-driven computation. However, existing SNN…
CIMple: Standard-cell SRAM-based CIM with LUT-based split softmax for attention acceleration
Bas Ahn, Xingjian Tao, Manil Dev Gomony +2
Large Language Models (LLMs) such as LLaMA and DeepSeek, are built on transformer architectures, which have become a standard model for achieving state-of-the-art performance in na…
LOREN: Low Rank-Based Code-Rate Adaptation in Neural Receivers
Bram Van Bolderik, Vlado Menkovski, Sonia Heemstra de Groot +1
Neural network based receivers have recently demonstrated superior system-level performance compared to traditional receivers. However, their practicality is limited by high memory…
LOKI: a 0.266 pJ/SOP Digital SNN Accelerator with Multi-Cycle Clock-Gated SRAM in 22nm
Rick Luiken, Lorenzo Pes, Manil Dev Gomony +1
Bio-inspired sensors like Dynamic Vision Sensors (DVS) and silicon cochleas are often combined with Spiking Neural Networks (SNNs), enabling efficient, event-driven processing simi…
Fibbinary-Based Compression and Quantization for Efficient Neural Radio Receivers
Roberta Fiandaca, Manil Dev Gomony
Neural receivers have shown outstanding performance compared to the conventional ones but this comes with a high network complexity leading to a heavy computational cost. This pose…
LinkBo: An Adaptive Single-Wire, Low-Latency, and Fault-Tolerant Communications Interface for Variable-Distance Chip-to-Chip Systems
Bochen Ye, Gustavo Naspolini, Kimmo Salo +1
Cost-effective embedded systems necessitate utilizing the single-wire communication protocol for inter-chip communication, thanks to its reduced pin count in comparison to the mult…