activity
20162026
most citedThe Cost of Application-Class Processing: Energy and Performance Analysis of a Linux-ready 1.7GHz 64bit RISC-V Core in 22nm FDSOI Technology

428 citations · 4.5k across the 191 of their papers we have counts for

collaborators
Showing 2017Show all

11 papers · 1 filter

cs.AR2017★ 30 cited

HERO: Heterogeneous Embedded Research Platform for Exploring RISC-V Manycore Accelerators on FPGA

Andreas Kurth, Pirmin Vogel, Alessandro Capotondi +2

Heterogeneous embedded systems on chip (HESoCs) co-integrate a standard host processor with programmable manycore accelerators (PMCAs) to combine general-purpose computing with dom…

cs.AR2017

An 826 MOPS, 210 uW/MHz Unum ALU in 65 nm

Florian Glaser, Stefan Mach, Abbas Rahimi +3

To overcome the limitations of conventional floating-point number formats, an interval arithmetic and variable-width storage format called universal number (unum) has been recently…

cs.NE2017★ 67 cited

NEURAghe: Exploiting CPU-FPGA Synergies for Efficient and Flexible CNN Inference Acceleration on Zynq SoCs

Paolo Meloni, Alessandro Capotondi, Gianfranco Deriu +5

Deep convolutional neural networks (CNNs) obtain outstanding results in tasks that require human-level understanding of data, like image or speech recognition. However, their compu…

cs.AR2017★ 1 cited

A Transprecision Floating-Point Platform for Ultra-Low Power Computing

Giuseppe Tagliavini, Stefan Mach, Davide Rossi +2

In modern low-power embedded platforms, floating-point (FP) operations emerge as a major contributor to the energy consumption of compute-intensive applications with large dynamic…

cs.OH2017

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

Manuele Rusci, Lukas Cavigelli, Luca Benini

Design automation in general, and in particular logic synthesis, can play a key role in enabling the design of application-specific Binarized Neural Networks (BNN). This paper pres…

eess.IV2017

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

Manuel Eggimann, Christelle Gloor, Florian Scheidegger +4

Many modern video processing pipelines rely on edge-aware (EA) filtering methods. However, recent high-quality methods are challenging to run in real-time on embedded hardware due…