84 citations · 107 across the 5 of their papers we have counts for
5 papers
A Multiplication-Free Spike-Time Learning Algorithm and its Efficient FPGA Implementation for On-Chip SNN Training
Maryam Mirsadeghi, Mojtaba Mirbagheri, Saeed Reza Kheradpisheh
Spiking Neural Networks (SNNs) offer a biologically inspired foundation for low-power, event-driven intelligence, yet their direct on-chip supervised training remains a key hardwar…
An FPGA-Based SoC Architecture with a RISC-V Controller for Energy-Efficient Temporal-Coding Spiking Neural Networks
Mohammad Javad Sekonji, Ali Mahani, Maryam Mirsadeghi +1
Spiking Neural Networks (SNNs) offer high energy efficiency and event-driven computation, ideal for low-power edge AI. Their hardware implementation on FPGAs, however, faces challe…
Spiking neural networks trained via proxy
Saeed Reza Kheradpisheh, Maryam Mirsadeghi, Timothée Masquelier
We propose a new learning algorithm to train spiking neural networks (SNN) using conventional artificial neural networks (ANN) as proxy. We couple two SNN and ANN networks, respect…
Spike time displacement based error backpropagation in convolutional spiking neural networks
Maryam Mirsadeghi, Majid Shalchian, Saeed Reza Kheradpisheh +1
We recently proposed the STiDi-BP algorithm, which avoids backward recursive gradient computation, for training multi-layer spiking neural networks (SNNs) with single-spike-based t…
BS4NN: Binarized Spiking Neural Networks with Temporal Coding and Learning
Saeed Reza Kheradpisheh, Maryam Mirsadeghi, Timothée Masquelier
We recently proposed the S4NN algorithm, essentially an adaptation of backpropagation to multilayer spiking neural networks that use simple non-leaky integrate-and-fire neurons and…