Search Results for author:
Found 6 papers, 2 papers with code
Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors
This poses a potential risk to user privacy.
Neural Network Compression for Noisy Storage Devices
Compression and efficient storage of neural network (NN) parameters is critical for applications that run on resource-constrained devices.
Noisy Neural Network Compression for Analog Storage Devices
Efficient compression and storage of neural network (NN) parameters is critical for resource-constrained, downstream machine learning applications.
Synthesizing Number Generators for Stochastic Computing using Mixed Integer Programming
Stochastic computing (SC) is a high density, low-power computation technique which encodes values as unary bitstreams instead of binary-encoded (BE) values.
Emerging Technologies
Stochastic Synthesis for Stochastic Computing
Stochastic computing (SC) is an emerging computing technique which offers higher computational density, and lower power over binary-encoded (BE) computation.
Emerging Technologies
MATIC: Learning Around Errors for Efficient Low-Voltage Neural Network Accelerators
As a result of the increasing demand for deep neural network (DNN)-based services, efforts to develop dedicated hardware accelerators for DNNs are growing rapidly.
