13 citations · 19 across the 8 of their papers we have counts for
15 papers
STeP-CiM: Strain-enabled Ternary Precision Computation-in-Memory based on Non-Volatile 2D Piezoelectric Transistors
Niharika Thakuria, Reena Elangovan, Sandeep K Thirumala +2
We propose 2D Piezoelectric FET (PeFET) based compute-enabled non-volatile memory for ternary deep neural networks (DNNs). PeFETs consist of a material with ferroelectric and piezo…
PIM-DRAM: Accelerating Machine Learning Workloads using Processing in Commodity DRAM
Sourjya Roy, Mustafa Ali, Anand Raghunathan
Deep Neural Networks (DNNs) have transformed the field of machine learning and are widely deployed in many applications involving image, video, speech and natural language processi…
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks
Jacob R. Stevens, Dipankar Das, Sasikanth Avancha +2
Graph Neural Networks (GNNs) use a fully-connected layer to extract features from the nodes of a graph and aggregate these features using message passing between nodes, combining t…
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers
Jacob R. Stevens, Rangharajan Venkatesan, Steve Dai +2
Transformers have transformed the field of natural language processing. This performance is largely attributed to the use of stacked self-attention layers, each of which consists o…
HW/SW Framework for Improving the Safety of Implantable and Wearable Medical Devices
Malin Prematilake, Younghyun Kim, Vijay Raghunathan +2
Implantable and wearable medical devices (IWMDs) are widely used for the monitoring and therapy of an increasing range of medical conditions. Improvements in medical devices, enabl…
Ax-BxP: Approximate Blocked Computation for Precision-Reconfigurable Deep Neural Network Acceleration
Reena Elangovan, Shubham Jain, Anand Raghunathan
Precision scaling has emerged as a popular technique to optimize the compute and storage requirements of Deep Neural Networks (DNNs). Efforts toward creating ultra-low-precision (s…