Neuromorphic Systems-on-Chip (NSoCs) are becoming heterogeneous by
integ...
Guillain-Barre syndrome is a rare neurological condition in which the hu...
Reconstructing perceived images from human brain activity monitored by
f...
Feedback-driven recurrent spiking neural networks (RSNNs) are powerful
c...
We propose a design methodology to facilitate fault tolerance of deep
le...
Neuromorphic hardware platforms can significantly lower the energy overh...
Precise monitoring of respiratory rate in premature infants is essential...
Recently, both industry and academia have proposed several different
neu...
Non-Volatile Memory (NVM) cells are used in neuromorphic hardware to sto...
As spiking-based deep learning inference applications are increasing in
...
We present a design-technology tradeoff analysis in implementing
machine...
The design of many-core neuromorphic hardware is getting more and more
c...
Spiking Neural Networks (SNN) are an emerging computation model, which u...
Non-Volatile Memories (NVMs) such as Resistive RAM (RRAM) are used in
ne...
Neuromorphic computing systems uses non-volatile memory (NVM) to impleme...
Recently, both industry and academia have proposed many different
neurom...
Neuromorphic computing systems such as DYNAPs and Loihi have recently be...
Artificial intelligence (AI) and Machine Learning (ML) are becoming perv...
Neuromorphic computing systems are embracing memristors to implement hig...
A common problem in bioinformatics is related to identifying gene regula...
Modern computing systems are embracing non-volatile memory (NVM) to impl...
Spiking Neural Networks (SNNs) are efficient computation models to perfo...
Phase-change memory (PCM) is a scalable and low latency non-volatile mem...
Based on the BioBricks standard, restriction synthesis is a novel catabo...
Hardware implementation of neuromorphic computing can significantly impr...
Neuromorphic architectures built with Non-Volatile Memory (NVM) can
sign...
With growing model complexity, mapping Spiking Neural Network (SNN)-base...
Neuromorphic computing with non-volatile memory (NVM) can significantly
...
In this paper, we propose a design methodology to partition and map the
...
As process technology continues to scale aggressively, circuit aging in ...
A prominent characteristic of write operation in Phase-Change Memory (PC...
Modern computing systems are embracing hybrid memory comprising of DRAM ...
Machine learning applications that are implemented with spike-based
comp...
We present PyCARL, a PyNN-based common Python programming interface for
...
Neuromorphic hardware with non-volatile memory (NVM) can implement machi...
Neuromorphic hardware platforms implement biological neurons and synapse...
Phase-change memory (PCM) devices have multiple banks to serve memory
re...
Spiking Neural Networks (SNNs) are widely deployed to solve complex patt...
Heartbeat classification using electrocardiogram (ECG) data is a vital
a...
Sensor calibration is one of the fundamental challenges in large-scale I...
Heart-rate estimation is a fundamental feature of modern wearable device...