Lukas Cavigelli

research

∙ 05/31/2023

ReDSEa: Automated Acceleration of Triangular Solver on Supercloud Heterogeneous Systems

When utilized effectively, Supercloud heterogeneous systems have the pot...

0 Georgios Zacharopoulos, et al. ∙

research

∙ 05/08/2023

Flex-SFU: Accelerating DNN Activation Functions by Non-Uniform Piecewise Approximation

Modern DNN workloads increasingly rely on activation functions consistin...

0 Enrico Reggiani, et al. ∙

research

∙ 10/17/2022

A ”New Ara” for Vector Computing: An Open Source Highly Efficient RISC-V V 1.0 Vector Processor Design

Vector architectures are gaining traction for highly efficient processin...

0 Matteo Perotti, et al. ∙

research

∙ 09/26/2022

Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile

Most of today's computer vision pipelines are built around deep neural n...

5 Renzo Andri, et al. ∙

research

∙ 02/14/2022

Vau da muntanialas: Energy-efficient multi-die scalable acceleration of RNN inference

Recurrent neural networks such as Long Short-Term Memories (LSTMs) learn...

5 Gianna Paulin, et al. ∙

research

∙ 01/10/2022

Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and Binary Neural Networks

Keyword spotting (KWS) is a crucial function enabling the interaction wi...

10 Gianmarco Cerutti, et al. ∙

research

∙ 05/04/2021

Reinforcement Learning for Scalable Logic Optimization with Graph Neural Networks

Logic optimization is an NP-hard problem commonly approached through han...

0 Xavier Timoneda, et al. ∙

research

∙ 03/25/2021

ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Network

Personalized ubiquitous healthcare solutions require energy-efficient we...

0 Thorir Mar Ingolfsson, et al. ∙

research

∙ 01/12/2021

Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices

Sound event detection (SED) is a hot topic in consumer and smart city ap...

0 Gianmarco Cerutti, et al. ∙

research

∙ 11/03/2020

CUTIE: Beyond PetaOp/s/W Ternary DNN Inference Acceleration with Better-than-Binary Energy Efficiency

We present a 3.1 POp/s/W fully digital hardware accelerator for ternary ...

0 Moritz Scherer, et al. ∙

research

∙ 05/31/2020

EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces

In recent years, deep learning (DL) has contributed significantly to the...

0 Thorir Mar Ingolfsson, et al. ∙

research

∙ 05/12/2020

ChewBaccaNN: A Flexible 223 TOPS/W BNN Accelerator

Binary Neural Networks enable smart IoT devices, as they significantly r...

0 Renzo Andri, et al. ∙

research

∙ 04/24/2020

Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNet Implementation for Edge Motor-Imagery Brain–Machine Interfaces

Motor-Imagery Brain-Machine Interfaces (MI-BMIs) promise direct and acce...

0 Tibor Schneider, et al. ∙

research

∙ 01/04/2020

RPR: Random Partition Relaxation for Training; Binary and Ternary Weight Neural Networks

We present Random Partition Relaxation (RPR), a method for strong quanti...

0 Lukas Cavigelli, et al. ∙

research

∙ 12/10/2019

HR-SAR-Net: A Deep Neural Network for Urban Scene Segmentation from High-Resolution SAR Data

Synthetic aperture radar (SAR) data is becoming increasingly available t...

0 Xiaying Wang, et al. ∙

research

∙ 11/08/2019

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

The growing number of low-power smart devices in the Internet of Things ...

15 Xiaying Wang, et al. ∙

research

∙ 08/30/2019

EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators

In the wake of the success of convolutional neural networks in image cla...

11 Lukas Cavigelli, et al. ∙

research

∙ 05/24/2019

Additive Noise Annealing and Approximation Properties of Quantized Neural Networks

We present a theoretical and experimental investigation of the quantizat...

0 Matteo Spallanzani, et al. ∙

research

∙ 10/01/2018

Extended Bit-Plane Compression for Convolutional Neural Network Accelerators

After the tremendous success of convolutional neural networks in image c...

0 Lukas Cavigelli, et al. ∙

research

∙ 08/15/2018

CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional Network Inference on Video Streams

The last few years have brought advances in computer vision at an amazin...

0 Lukas Cavigelli, et al. ∙

research

∙ 03/05/2018

Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine

Deep neural networks have achieved impressive results in computer vision...

0 Renzo Andri, et al. ∙

research

∙ 03/05/2018

Hyperdrive: A Systolically Scalable Binary-Weight CNN Inference Engine for mW IoT End-Nodes

Deep neural networks have achieved impressive results in computer vision...

0 Renzo Andri, et al. ∙

research

∙ 03/05/2018

XNORBIN: A 95 TOp/s/W Hardware Accelerator for Binary Convolutional Neural Networks

Deploying state-of-the-art CNNs requires power-hungry processors and off...

0 Andrawes Al Bahou, et al. ∙

research

∙ 11/21/2017

Design Automation for Binarized Neural Networks: A Quantum Leap Opportunity?

Design automation in general, and in particular logic synthesis, can pla...

0 Manuele Rusci, et al. ∙

research

∙ 11/15/2017

Chipmunk: A Systolically Scalable 0.9 mm^2, 3.08 Gop/s/mW @ 1.2 mW Accelerator for Near-Sensor Recurrent Neural Network Inference

Recurrent neural networks (RNNs) are state-of-the-art in voice awareness...

0 Francesco Conti, et al. ∙

research

∙ 11/08/2017

Hydra: An Accelerator for Real-Time Edge-Aware Permeability Filtering in 65nm CMOS

Many modern video processing pipelines rely on edge-aware (EA) filtering...

0 Manuel Eggimann, et al. ∙

research

∙ 09/28/2017

Efficient Convolutional Neural Network For Audio Event Detection

Wireless distributed systems as used in sensor networks, Internet-of-Thi...

0 Matthias Meyer, et al. ∙

research

∙ 04/14/2017

CBinfer: Change-Based Inference for Convolutional Neural Networks on Video Data

Extracting per-frame features using convolutional neural networks for re...

0 Lukas Cavigelli, et al. ∙

research

∙ 04/03/2017

Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations

We present a new approach to learn compressible representations in deep ...

0 Eirikur Agustsson, et al. ∙

research

∙ 11/22/2016

CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression

Lossy image compression algorithms are pervasively used to reduce the si...

0 Lukas Cavigelli, et al. ∙

research

∙ 11/09/2016

Computationally Efficient Target Classification in Multispectral Image Data with Deep Neural Networks

Detecting and classifying targets in video streams from surveillance cam...

0 Lukas Cavigelli, et al. ∙

research

∙ 09/26/2016

Deep Structured Features for Semantic Segmentation

We propose a highly structured neural network architecture for semantic ...

0 Michael Tschannen, et al. ∙

research

∙ 06/17/2016

YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration

Convolutional neural networks (CNNs) have revolutionized the world of co...

0 Renzo Andri, et al. ∙

research

∙ 12/14/2015

Origami: A 803 GOp/s/W Convolutional Network Accelerator

An ever increasing number of computer vision and image/video processing ...

0 Lukas Cavigelli, et al. ∙

Lukas Cavigelli

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