Search Results for author:
Found 9 papers, 2 papers with code
Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction
This problem has been studied in a classical abstract model, the HP model, where the protein is modeled as a sequence of H (hydrophobic) and P (polar) amino acids on a lattice.
PIM-QAT: Neural Network Quantization for Processing-In-Memory (PIM) Systems
In this paper, we propose a method for training quantized networks to incorporate PIM quantization, which is ubiquitous to all PIM systems.
F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization
Our approach achieves comparable and better performance, when compared not only to existing quantization techniques with INT32 multiplication or floating-point arithmetic, but also to the full-precision counterparts, achieving state-of-the-art performance.
Magnetoelectric Bio-Implants Powered and Programmed by a Single Transmitter for Coordinated Multisite Stimulation
This article presents a hardware platform including stimulating implants wirelessly powered and controlled by a shared transmitter (TX) for coordinated leadless multisite stimulation.
SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis
Specifically, in terms of usability, it is demanding for non-experts to implement deep learning models, obtain the right settings for the entire machine learning pipeline, manage models and datasets, and exploit external data sources all together.
A Self-Regulated and Reconfigurable CMOS Physically Unclonable Function Featuring Zero-Overhead Stabilization
Thanks to the highly stable and self-regulated PUF cell and the zero-overhead stabilization scheme, a 0. 00182% native BER is obtained after reconfiguration.
MagNI: A Magnetoelectrically Powered and Controlled Wireless Neurostimulating Implant
This paper presents the first wireless and programmable neural stimulator leveraging magnetoelectric (ME) effects for power and data transfer.
CAP-RAM: A Charge-Domain In-Memory Computing 6T-SRAM for Accurate and Precision-Programmable CNN Inference
A compact, accurate, and bitwidth-programmable in-memory computing (IMC) static random-access memory (SRAM) macro, named CAP-RAM, is presented for energy-efficient convolutional neural network (CNN) inference.
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed.
