10 citations · 10 across the 3 of their papers we have counts for
5 papers
Cross-Layer Co-Design of Low-Latency Stateful Logic Adders with Printed Memristors
Fabian Seiler, Kerem Kagan Güclü, Hongrong Hu +3
Data movement between memory and processing units remains a major bottleneck in data-centric systems. Memristive Stateful Logic (SL) addresses this problem by performing Boolean op…
FAME: An FPGA-Based Platform for Approximate Multipliers Evaluation with Pattern-Guided DNN Retraining
Rappy Saha, Nima Amirafshar, Jude Haris +2
Approximate multipliers can reduce hardware area and energy consumption in Deep Neural Network (DNN) inference; however, they introduce computational errors. Assessing the accuracy…
An Instruction Set Architecture for IMPLY-based Memristive Processing-in-Array
Liam Splittgerber, Fabian Seiler, Nima TaheriNejad
The push towards expanded ultra-low-power edge computing necessitates hardware capable of operating under extremely strict energy constraints. Traditional Complementary Metal-Oxide…
Three factor delay learning rules for spiking neural networks
Luke Vassallo, Nima Taherinejad
Spiking Neural Networks (SNNs) are dynamical systems that operate on spatiotemporal data, yet their learnable parameters are often limited to synaptic weights, contributing little…
Learning on Hardware: A Tutorial on Neural Network Accelerators and Co-Processors
Lukas Baischer, Matthias Wess, Nima TaheriNejad
Deep neural networks (DNNs) have the advantage that they can take into account a large number of parameters, which enables them to solve complex tasks. In computer vision and speec…