217 citations · 283 across the 19 of their papers we have counts for
20 papers
SwiftTron: An Efficient Hardware Accelerator for Quantized Transformers
Alberto Marchisio, Davide Dura, Maurizio Capra +3
Transformers' compute-intensive operations pose enormous challenges for their deployment in resource-constrained EdgeAI / tinyML devices. As an established neural network compressi…
Custom Memory Design for Logic-in-Memory: Drawbacks and Improvements over Conventional Memories
Fabrizio Ottati, Giovanna Turvani, Marco Vacca +1
The speed of modern digital systems is severely limited by memory latency (the ``Memory Wall'' problem). Data exchange between Logic and Memory is also responsible for a large part…
LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor
Alberto Viale, Alberto Marchisio, Maurizio Martina +2
Autonomous Driving (AD) related features represent important elements for the next generation of mobile robots and autonomous vehicles focused on increasingly intelligent, autonomo…
CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks
Muhammad Abdullah Hanif, Giuseppe Maria Sarda, Alberto Marchisio +3
In today's era of smart cyber-physical systems, Deep Neural Networks (DNNs) have become ubiquitous due to their state-of-the-art performance in complex real-world applications. The…
Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations
Alberto Marchisio, Beatrice Bussolino, Edoardo Salvati +3
Complex Deep Neural Networks such as Capsule Networks (CapsNets) exhibit high learning capabilities at the cost of compute-intensive operations. To enable their deployment on edge…
R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors
Alberto Marchisio, Giacomo Pira, Maurizio Martina +2
Spiking Neural Networks (SNNs) aim at providing energy-efficient learning capabilities when implemented on neuromorphic chips with event-based Dynamic Vision Sensors (DVS). This pa…