Jason Eshraghian

About

Jason Eshraghian is an Assistant Professor at the Department of Electrical and Computer Engineering at the University of California, Santa Cruz. His research focuses on brain-inspired circuit design aimed at accelerating artificial intelligence algorithms and spiking neural networks. He leads the UCSC Neuromorphic Computing Group, which is dedicated to advancing neuromorphic engineering and computing technologies. For ongoing research updates, the lab maintains a dedicated webpage at ncg.ucsc.edu.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Computer architecture
• Electronic engineering
• Neuroscience
• Psychology
• Embedded system
• Cognitive science
• Computer hardware

Selected publications

• Training Spiking Neural Networks Using Lessons From Deep Learning

  Proceedings of the IEEE · 2023 · 647 citations

  1st authorCorresponding

  The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This article serves as a tutorial and perspective showing how to apply the lessons learned from several decades of research in deep learning, gradient descent, backpropagation, and neuroscience to biologically plausible spiking neural networks (SNNs). We also explore the delicate int…

  DOI

• Spiking neural networks on FPGA: A survey of methodologies and recent advancements

  Neural Networks · 2025-02-14 · 21 citations

  reviewOpen accessSenior author
  PublisherOA PDFDOI

• ON-OFF neuromorphic ISING machines using Fowler-Nordheim annealers

  Nature Communications · 2025-03-31 · 9 citations

  articleOpen access

  We introduce NeuroSA, a neuromorphic architecture specifically designed to ensure asymptotic convergence to the ground state of an Ising problem using a Fowler-Nordheim quantum mechanical tunneling based threshold-annealing process. The core component of NeuroSA consists of a pair of asynchronous ON-OFF neurons, which effectively map classical simulated annealing dynamics onto a network of integrate-and-fire neurons. The threshold of each ON-OFF neuron pair is adaptively adjusted by an FN anneal…

  PublisherOA PDFDOI

• Dynamic spatio-temporal pruning for efficient spiking neural networks

  Frontiers in Neuroscience · 2025-03-25 · 8 citations

  articleOpen access

  Spiking neural networks (SNNs), which draw from biological neuron models, have the potential to improve the computational efficiency of artificial neural networks (ANNs) due to their event-driven nature and sparse data flow. SNNs rely on dynamical sparsity, in that neurons are trained to activate sparsely to minimize data communication. This is critical when accounting for hardware given the bandwidth limitations between memory and processor. Given that neurons are sparsely activated, weights ar…

  PublisherOA PDFDOI

• A predictive approach to enhance time-series forecasting

  Nature Communications · 2025-09-30 · 6 citations

  articleOpen accessSenior author

  Accurate time-series forecasting is crucial in various scientific and industrial domains, yet deep learning models often struggle to capture long-term dependencies and adapt to data distribution shifts over time. We introduce Future-Guided Learning, an approach that enhances time-series event forecasting through a dynamic feedback mechanism inspired by predictive coding. Our method involves two models: a detection model that analyzes future data to identify critical events and a forecasting mode…

  PublisherOA PDFDOI

Frequent coauthors

• Herbert Ho‐Ching Iu

  University of Western Australia

  74 shared

• Omid Kavehei

  University of Sydney

  56 shared

• Xiaoyuan Wang

  China Aerospace Science and Technology Corporation

  53 shared

• Armin Nikpour

  Royal Prince Alfred Hospital

  50 shared

• Nhan Duy Truong

  University of Sydney

  49 shared

• Mostafa Rahimi Azghadi

  39 shared

• Sung-Mo Kang

  37 shared

• Corey Lammie

  IBM Research - Zurich

  34 shared

Labs

• Neuromorphic Computing GroupPI

Education

• Bachelor of Laws & Electronic Engineering (Combined Degree)

  University of Western Australia

  2016

Awards & honors

• 2023 IEEE CAS Darlington Best Paper Award
• 2020 IEEE ICECS Best Live Demonstration Award
• 2019 IEEE TVLSI Best Paper Award
• 2019 IEEE AICAS Best Paper Award
• Fulbright Research Fellowship (Australian-American Fulbright…

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