107 citations · 146 across the 10 of their papers we have counts for
10 papers
Longer Attention Span: Increasing Transformer Context Length with Sparse Graph Processing Techniques
Nathaniel Tomczak, Sanmukh Kuppannagari
Transformers have demonstrated great success in numerous domains including natural language processing and bioinformatics. This success stems from the use of the attention mechanis…
A Framework to Enable Algorithmic Design Choice Exploration in DNNs
Timothy L. Cronin, Sanmukh Kuppannagari
Deep learning technologies, particularly deep neural networks (DNNs), have demonstrated significant success across many domains. This success has been accompanied by substantial ad…
Efficient Quantum Circuit Cutting by Neglecting Basis Elements
Daniel T. Chen, Ethan H. Hansen, Xinpeng Li +7
Quantum circuit cutting has been proposed to help execute large quantum circuits using only small and noisy machines. Intuitively, cutting a qubit wire can be thought of as classic…
BRAC+: Improved Behavior Regularized Actor Critic for Offline Reinforcement Learning
Chi Zhang, Sanmukh Rao Kuppannagari, Viktor K Prasanna
Online interactions with the environment to collect data samples for training a Reinforcement Learning (RL) agent is not always feasible due to economic and safety concerns. The go…
Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations
Chi Zhang, Sanmukh Rao Kuppannagari, Viktor K Prasanna
Reinforcement Learning (RL) has achieved significant success in application domains such as robotics, games and health care. However, training RL agents is very time consuming. Cur…
A High Throughput Parallel Hash Table on FPGA using XOR-based Memory
Ruizhi Zhang, Sasindu Wijeratne, Yang Yang +2
Hash table is a fundamental data structure for quick search and retrieval of data. It is a key component in complex graph analytics and AI/ML applications. State-of-the-art paralle…
DYNAMAP: Dynamic Algorithm Mapping Framework for Low Latency CNN Inference
Yuan Meng, Sanmukh Kuppannagari, Rajgopal Kannan +1
Most of the existing work on FPGA acceleration of Convolutional Neural Network (CNN) focus on employing a single strategy (algorithm, dataflow, etc.) across all the layers. Such an…
Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors
Chi Zhang, Sanmukh Rao Kuppannagari, Viktor K Prasanna
Model usage is the central challenge of model-based reinforcement learning. Although dynamics model based on deep neural networks provide good generalization for single step predic…
Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation
Chi Zhang, Sanmukh R. Kuppannagari, Rajgopal Kannan +1
Buildings sector is one of the major consumers of energy in the United States. The buildings HVAC (Heating, Ventilation, and Air Conditioning) systems, whose functionality is to ma…
Optimal Net-Load Balancing in Smart Grids with High PV Penetration
Sanmukh R. Kuppannagari, Rajgopal Kannan, Viktor K. Prasanna
Mitigating Supply-Demand mismatch is critical for smooth power grid operation. Traditionally, load curtailment techniques such as Demand Response (DR) have been used for this purpo…