Antonio Pullini

A ultra-low-energy convolution engine for fast brain-inspired vision in multicore clusters

Deep learning for sensor-based activity recognition: A survey

CNN based approach for activity recognition using a wrist-worn accelerometer

Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks

Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey

Industrial IoT for Intelligent Steelmaking With Converter Mouth Flame Spectrum Information Processed by Deep Learning

An Architecture to Accelerate Convolution in Deep Neural Networks

XNOR Neural Engine: A Hardware Accelerator IP for 21.6-fJ/op Binary Neural Network Inference

Energy-efficient vision on the PULP platform for ultra-low power parallel computing

A Heterogeneous Multicore System on Chip for Energy Efficient Brain Inspired Computing

μDMA: An autonomous I/O subsystem for IoT end-nodes