72 citations · 74 across the 8 of their papers we have counts for
11 papers
Making Analog Training Scale: Co-Designing Mapping, Optimizer, and Converters
Zhaoxian Wu, Tayfun Gokmen, Omobayode Fagbohungbe +2
Analog in-memory computing (AIMC) offers an alternative for model training by executing matrix operations directly where weights are stored. However, scaling AIMC to train modern d…
Incorruptible Neural Networks: Training Models that can Generalize to Large Internal Perturbations
Philip Jacobson, Ben Feinberg, Suhas Kumar +3
Flat regions of the neural network loss landscape have long been hypothesized to correlate with better generalization properties. A closely related but distinct problem is training…
A self-heating electrochemical cell with nine decades of programmable linear resistance
Adam L. Gross, Sangheon Oh, Minseong Park +22
A programmable linear resistor with a compact footprint would have profound implications for microelectronics, enabling efficient in-sensor analog signal processing and in-memory c…
Analog fast Fourier transforms for scalable and efficient signal processing
T. Patrick Xiao, Ben Feinberg, David K. Richardson +7
Edge devices are being deployed at increasing volumes to sense and act on information from the physical world. The discrete Fourier transform (DFT) is often necessary to make this…
Parallel Matrix Multiplication Using Voltage Controlled Magnetic Anisotropy Domain Wall Logic
Nicholas Zogbi, Samuel Liu, Christopher H. Bennett +4
The domain wall-magnetic tunnel junction (DW-MTJ) is a versatile device that can simultaneously store data and perform computations. These three-terminal devices are promising for…
An out-of-distribution discriminator based on Bayesian neural network epistemic uncertainty
Ethan Ancell, Christopher Bennett, Bert Debusschere +3
Neural networks have revolutionized the field of machine learning with increased predictive capability. In addition to improving the predictions of neural networks, there is a simu…
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…
On the Accuracy of Analog Neural Network Inference Accelerators
T. Patrick Xiao, Ben Feinberg, Christopher H. Bennett +5
Specialized accelerators have recently garnered attention as a method to reduce the power consumption of neural network inference. A promising category of accelerators utilizes non…
Device-aware inference operations in SONOS nonvolatile memory arrays
Christopher H. Bennett, T. Patrick Xiao, Ryan Dellana +10
Non-volatile memory arrays can deploy pre-trained neural network models for edge inference. However, these systems are affected by device-level noise and retention issues. Here, we…
Evaluating complexity and resilience trade-offs in emerging memory inference machines
Christopher H. Bennett, Ryan Dellana, T. Patrick Xiao +6
Neuromorphic-style inference only works well if limited hardware resources are maximized properly, e.g. accuracy continues to scale with parameters and complexity in the face of po…