75 citations · 206 across the 28 of their papers we have counts for
30 papers
Evaluation of STT-MRAM as a Scratchpad for Training in ML Accelerators
Sourjya Roy, Cheng Wang, Anand Raghunathan
Progress in artificial intelligence and machine learning over the past decade has been driven by the ability to train larger deep neural networks (DNNs), leading to a compute deman…
X-Former: In-Memory Acceleration of Transformers
Shrihari Sridharan, Jacob R. Stevens, Kaushik Roy +1
Transformers have achieved great success in a wide variety of natural language processing (NLP) tasks due to the attention mechanism, which assigns an importance score for every wo…
Approximate Computing and the Efficient Machine Learning Expedition
Jörg Henkel, Hai Li, Anand Raghunathan +4
Approximate computing (AxC) has been long accepted as a design alternative for efficient system implementation at the cost of relaxed accuracy requirements. Despite the AxC researc…
A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networks
Wilfried Haensch, Anand Raghunathan, Kaushik Roy +4
Deep Learning neural networks are pervasive, but traditional computer architectures are reaching the limits of being able to efficiently execute them for the large workloads of tod…
Piezoelectric Strain FET (PeFET) based Non-Volatile Memories
Niharika Thakuria, Reena Elangovan, Anand Raghunathan +1
We propose non-volatile memory (NVM) designs based on Piezoelectric Strain FET (PeFET) utilizing a piezoelectric/ferroelectric (PE/FE such as PZT) coupled with 2D Transition Metal…
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…
AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP Models
Amrit Nagarajan, Sanchari Sen, Jacob R. Stevens +1
Transformers have greatly advanced the state-of-the-art in Natural Language Processing (NLP) in recent years, but present very large computation and storage requirements. We observ…
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks
Sarada Krithivasan, Sanchari Sen, Anand Raghunathan
Adversarial attacks have exposed serious vulnerabilities in Deep Neural Networks (DNNs) through their ability to force misclassifications through human-imperceptible perturbations…
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks
Sanchari Sen, Balaraman Ravindran, Anand Raghunathan
Ensuring robustness of Deep Neural Networks (DNNs) is crucial to their adoption in safety-critical applications such as self-driving cars, drones, and healthcare. Notably, DNNs are…
Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks
Sourjya Roy, Priyadarshini Panda, Gopalakrishnan Srinivasan +1
Modern deep networks have millions to billions of parameters, which leads to high memory and energy requirements during training as well as during inference on resource-constrained…
TxSim:Modeling Training of Deep Neural Networks on Resistive Crossbar Systems
Sourjya Roy, Shrihari Sridharan, Shubham Jain +1
Resistive crossbars have attracted significant interest in the design of Deep Neural Network (DNN) accelerators due to their ability to natively execute massively parallel vector-m…
Gradual Channel Pruning while Training using Feature Relevance Scores for Convolutional Neural Networks
Sai Aparna Aketi, Sourjya Roy, Anand Raghunathan +1
The enormous inference cost of deep neural networks can be scaled down by network compression. Pruning is one of the predominant approaches used for deep network compression. Howev…
Valley-Coupled-Spintronic Non-Volatile Memories with Compute-In-Memory Support
Sandeep Thirumala, Yi-Tse Hung, Shubham Jain +6
In this work, we propose valley-coupled spin-hall memories (VSH-MRAMs) based on monolayer WSe2. The key features of the proposed memories are (a) the ability to switch magnets with…
TiM-DNN: Ternary in-Memory accelerator for Deep Neural Networks
Shubham Jain, Sumeet Kumar Gupta, Anand Raghunathan
The use of lower precision has emerged as a popular technique to optimize the compute and storage requirements of complex Deep Neural Networks (DNNs). In the quest for lower precis…
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing
Athindran Ramesh Kumar, Balaraman Ravindran, Anand Raghunathan
Object detection in videos is an important task in computer vision for various applications such as object tracking, video summarization and video search. Although great progress h…
RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars
Shubham Jain, Abhronil Sengupta, Kaushik Roy +1
Resistive crossbars designed with non-volatile memory devices have emerged as promising building blocks for Deep Neural Network (DNN) hardware, due to their ability to compactly an…
SparCE: Sparsity aware General Purpose Core Extensions to Accelerate Deep Neural Networks
Sanchari Sen, Shubham Jain, Swagath Venkataramani +1
Deep Neural Networks (DNNs) have emerged as the method of choice for solving a wide range of machine learning tasks. The enormous computational demands posed by DNNs have most comm…
DyVEDeep: Dynamic Variable Effort Deep Neural Networks
Sanjay Ganapathy, Swagath Venkataramani, Balaraman Ravindran +1
Deep Neural Networks (DNNs) have advanced the state-of-the-art in a variety of machine learning tasks and are deployed in increasing numbers of products and services. However, the…
Computing in Memory with Spin-Transfer Torque Magnetic RAM
Shubham Jain, Ashish Ranjan, Kaushik Roy +1
In-memory computing is a promising approach to addressing the processor-memory data transfer bottleneck in computing systems. We propose Spin-Transfer Torque Compute-in-Memory (STT…