research
∙
03/08/2023
autoXFPGAs: An End-to-End Automated Exploration Framework for Approximate Accelerators in FPGA-Based Systems
Generation and exploration of approximate circuits and accelerators has ...
share
research
∙
10/11/2022
RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks
Neural Architecture Search (NAS) algorithms aim at finding efficient Dee...
research
∙
06/27/2022
Designing Approximate Arithmetic Circuits with Combined Error Constraints
Approximate circuits trading the power consumption for the quality of re...
research
∙
05/06/2022
Optimization of BDD-based Approximation Error Metrics Calculations
Software methods introduced for automated design of approximate implemen...
research
∙
03/09/2022
ArithsGen: Arithmetic Circuit Generator for Hardware Accelerators
Generators of arithmetic circuits can automatically deliver various impl...
research
∙
01/28/2021
Evolutionary Neural Architecture Search Supporting Approximate Multipliers
There is a growing interest in automated neural architecture search (NAS...
research
∙
10/12/2020
DESCNet: Developing Efficient Scratchpad Memories for Capsule Network Hardware
Deep Neural Networks (DNNs) have been established as the state-of-the-ar...
research
∙
08/19/2020
NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks
Deep Neural Networks (DNNs) have made significant improvements to reach ...
research
∙
04/23/2020
Semantically-Oriented Mutation Operator in Cartesian Genetic Programming for Evolutionary Circuit Design
Despite many successful applications, Cartesian Genetic Programming (CGP...
research
∙
04/22/2020
ApproxFPGAs: Embracing ASIC-Based Approximate Arithmetic Components for FPGA-Based Systems
There has been abundant research on the development of Approximate Circu...
research
∙
04/22/2020
Using Libraries of Approximate Circuits in Design of Hardware Accelerators of Deep Neural Networks
Approximate circuits have been developed to provide good tradeoffs betwe...
research
∙
03/05/2020
Adaptive Verifiability-Driven Strategy for Evolutionary Approximation of Arithmetic Circuits
We present a novel approach for designing complex approximate arithmetic...
research
∙
02/21/2020
TFApprox: Towards a Fast Emulation of DNN Approximate Hardware Accelerators on GPU
Energy efficiency of hardware accelerators of deep neural networks (DNN)...
research
∙
12/02/2019
ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations
Recent advances in Capsule Networks (CapsNets) have shown their superior...
research
∙
06/11/2019
ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining
The state-of-the-art approaches employ approximate computing to improve ...
research
∙
03/11/2019
Automated Circuit Approximation Method Driven by Data Distribution
We propose an application-tailored data-driven fully automated method fo...
research
∙
02/22/2019
