115 citations · 120 across the 12 of their papers we have counts for
12 papers
SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks
Prasanna Date, Kevin Zhu, Shruti Kulkarni +12
Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing. However, their widespread adoption is hindered by a lack of fast, acce…
Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
Hyeongmeen Baik, Hamed Poursiami, Maryam Parsa +1
Always-on converter health monitoring demands sub-mW edge inference, a regime inaccessible to GPU-based physics-informed neural networks. This work separates spiking temporal proce…
Transductive Spiking Graph Neural Networks for Loihi
Shay Snyder, Victoria Clerico, Guojing Cong +4
Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements…
Zespol: A Lightweight Environment for Training Swarming Agents
Shay Snyder, Kevin Zhu, Ricardo Vega +2
Agent-based modeling (ABM) and simulation have emerged as important tools for studying emergent behaviors, especially in the context of swarming algorithms for robotic systems. Des…
Neuromorphic Bayesian Optimization in Lava
Shay Snyder, Sumedh R. Risbud, Maryam Parsa
The ever-increasing demands of computationally expensive and high-dimensional problems require novel optimization methods to find near-optimal solutions in a reasonable amount of t…
Object Motion Sensitivity: A Bio-inspired Solution to the Ego-motion Problem for Event-based Cameras
Shay Snyder, Hunter Thompson, Md Abdullah-Al Kaiser +3
Neuromorphic (event-based) image sensors draw inspiration from the human-retina to create an electronic device that can process visual stimuli in a way that closely resembles its b…
Simulate Less, Expect More: Bringing Robot Swarms to Life via Low-Fidelity Simulations
Ricardo Vega, Kevin Zhu, Sean Luke +2
This paper proposes a novel methodology for addressing the simulation-reality gap for multi-robot swarm systems. Rather than immediately try to shrink or `bridge the gap' anytime a…
Biological connectomes as a representation for the architecture of artificial neural networks
Samuel Schmidgall, Catherine Schuman, Maryam Parsa
Grand efforts in neuroscience are working toward mapping the connectomes of many new species, including the near completion of the Drosophila melanogaster. It is important to ask w…
IRIS: Integrated Retinal Functionality in Image Sensors
Zihan Yin, Md Abdullah-Al Kaiser, Lamine Ousmane Camara +5
Neuromorphic image sensors draw inspiration from the biological retina to implement visual computations in electronic hardware. Gain control in phototransduction and temporal diffe…
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment
Maryam Parsa, Catherine D. Schuman, Prasanna Date +8
Training neural networks for neuromorphic deployment is non-trivial. There have been a variety of approaches proposed to adapt back-propagation or back-propagation-like algorithms…
PABO: Pseudo Agent-Based Multi-Objective Bayesian Hyperparameter Optimization for Efficient Neural Accelerator Design
Maryam Parsa, Aayush Ankit, Amirkoushyar Ziabari +1
The ever increasing computational cost of Deep Neural Networks (DNN) and the demand for energy efficient hardware for DNN acceleration has made accuracy and hardware cost co-optimi…
Probabilistic Deep Spiking Neural Systems Enabled by Magnetic Tunnel Junction
Abhronil Sengupta, Maryam Parsa, Bing Han +1
Deep Spiking Neural Networks are becoming increasingly powerful tools for cognitive computing platforms. However, most of the existing literature on such computing models are devel…