89 citations · 180 across the 28 of their papers we have counts for
14 papers · 1 filter
A Multi-Threading Kernel for Enabling Neuromorphic Edge Applications
Lars Niedermeier, Vyom Shah, Jeffrey L. Krichmar
Spiking Neural Networks (SNNs) have sparse, event driven processing that can leverage neuromorphic applications. In this work, we introduce a multi-threading kernel that enables ne…
An Integrated Toolbox for Creating Neuromorphic Edge Applications
Lars Niedermeier, Jeffrey L. Krichmar
Spiking Neural Networks (SNNs) and neuromorphic models are more efficient and have more biological realism than the activation functions typically used in deep neural networks, tra…
A Sparse Quantized Hopfield Network for Online-Continual Memory
Nick Alonso, Jeff Krichmar
An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-indep…
Dynamic Reliability Management in Neuromorphic Computing
Shihao Song, Jui Hanamshet, Adarsha Balaji +5
Neuromorphic computing systems uses non-volatile memory (NVM) to implement high-density and low-energy synaptic storage. Elevated voltages and currents needed to operate NVMs cause…
NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks
Adarsha Balaji, Shihao Song, Twisha Titirsha +6
Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN). Consequent…
Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware
Twisha Titirsha, Shihao Song, Anup Das +4
Neuromorphic computing systems are embracing memristors to implement high density and low power synaptic storage as crossbar arrays in hardware. These systems are energy efficient…
Neuroevolution of a Recurrent Neural Network for Spatial and Working Memory in a Simulated Robotic Environment
Xinyun Zou, Eric O. Scott, Alexander B. Johnson +4
Animals ranging from rats to humans can demonstrate cognitive map capabilities. We evolved weights in a biologically plausible recurrent neural network (RNN) using an evolutionary…
Enabling Resource-Aware Mapping of Spiking Neural Networks via Spatial Decomposition
Adarsha Balaji, Shihao Song, Anup Das +5
With growing model complexity, mapping Spiking Neural Network (SNN)-based applications to tile-based neuromorphic hardware is becoming increasingly challenging. This is because the…
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
Adarsha Balaji, Prathyusha Adiraju, Hirak J. Kashyap +4
We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN). Through PyCARL, we make the following two k…
Attention-Based Structural-Plasticity
Soheil Kolouri, Nicholas Ketz, Xinyun Zou +2
Catastrophic forgetting/interference is a critical problem for lifelong learning machines, which impedes the agents from maintaining their previously learned knowledge while learni…
Neuromodulated Goal-Driven Perception in Uncertain Domains
Xinyun Zou, Soheil Kolouri, Praveen K. Pilly +1
In uncertain domains, the goals are often unknown and need to be predicted by the organism or system. In this paper, contrastive excitation backprop (c-EB) was used in a goal-drive…
Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning
Georgios Detorakis, Sadique Sheik, Charles Augustine +6
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware. However, neuromorphic implementations of embedded learning at large…
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Anup Das, Paruthi Pradhapan, Willemijn Groenendaal +7
Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram…
A Self-Driving Robot Using Deep Convolutional Neural Networks on Neuromorphic Hardware
Tiffany Hwu, Jacob Isbell, Nicolas Oros +1
Neuromorphic computing is a promising solution for reducing the size, weight and power of mobile embedded systems. In this paper, we introduce a realization of such a system by cre…