72 citations · 72 across the 5 of their papers we have counts for
6 papers
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…
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…
TCAM-SSD: A Framework for Search-Based Computing in Solid-State Drives
Ryan Wong, Nikita Kim, Kevin Higgs +4
As the amount of data produced in society continues to grow at an exponential rate, modern applications are incurring significant performance and energy penalties due to high data…
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…