Quantum noise is the key challenge in Noisy Intermediate-Scale Quantum (NISQ) computers. Previous work for mitigating noise has primarily focused on gate-level or pulse …
G Yuan, P Behnam, Z Li, A Shafiee… - 2021 ACM/IEEE 48th …, 2021 - ieeexplore.ieee.org
Recent work demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix …
As real-world graphs expand in size, larger GNN models with billions of parameters are deployed. High parameter count in such models makes training and inference on graphs …
E Ozen, A Orailoglu - … on Computer-Aided Design of Integrated …, 2022 - ieeexplore.ieee.org
The efficiency of machine intelligence techniques has improved noticeably in the embedded application domains thanks to the dedicated hardware accelerators for deep neural …
Neuromorphic computing uses spiking neuron network models to solve machine learning problems in a more energy-efficient way when compared to conventional artificial neural …
We propose a design methodology to facilitate fault tolerance of deep learning models. First, we implement a many-core fault-tolerant neuromorphic hardware design, where neuron and …
Stochastic rounding is a critical technique used in low-precision deep neural networks (DNNs) training to ensure good model accuracy. However, it requires a large number of …
H Lee, M Kim, D Min, J Kim, J Back… - 2022 55th IEEE/ACM …, 2022 - ieeexplore.ieee.org
The crossbar structure of the nonvolatile memory enables highly parallel and energy- efficient analog matrix-vector-multiply (MVM) operations. To exploit its efficiency, existing …