23 citations · 25 across the 5 of their papers we have counts for
5 papers
Stochastic Domain Wall-Magnetic Tunnel Junction Artificial Neurons for Noise-Resilient Spiking Neural Networks
Thomas Leonard, Samuel Liu, Harrison Jin +1
The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) make SNNs promising for edge applications that require high energy efficiency. To realize SNNs in h…
Domain Wall-Magnetic Tunnel Junction Analog Content Addressable Memory Using Current and Projected Data
Harrison Jin, Hanqing Zhu, Keren Zhu +8
With the rise in in-memory computing architectures to reduce the compute-memory bottleneck, a new bottleneck is present between analog and digital conversion. Analog content-addres…
Fuse and Mix: MACAM-Enabled Analog Activation for Energy-Efficient Neural Acceleration
Hanqing Zhu, Keren Zhu, Jiaqi Gu +4
Analog computing has been recognized as a promising low-power alternative to digital counterparts for neural network acceleration. However, conventional analog computing is mainly…
Metaplastic and Energy-Efficient Biocompatible Graphene Artificial Synaptic Transistors for Enhanced Accuracy Neuromorphic Computing
Dmitry Kireev, Samuel Liu, Harrison Jin +4
CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is…
Shape-Dependent Multi-Weight Magnetic Artificial Synapses for Neuromorphic Computing
Thomas Leonard, Samuel Liu, Mahshid Alamdar +8
In neuromorphic computing, artificial synapses provide a multi-weight conductance state that is set based on inputs from neurons, analogous to the brain. Additional properties of t…