7 citations · 13 across the 11 of their papers we have counts for
12 papers
Affective Agent: On-Device Personalized Intervention Reasoning for Wearable Systems
Reina Mun, Zishen Wan, Vijay Janapa Reddi
Affective computing has advanced wearable state inference, but on-device reasoning about whether, when, and how to intervene remains challenging. We present Affective Agent, a thre…
GenAI for Systems: Recurring Challenges and Design Principles from Software to Silicon
Arya Tschand, Chenyu Wang, Zishen Wan +21
Generative AI is reshaping how computing systems are designed, optimized, and built, yet research remains fragmented across software, architecture, and chip design communities. Thi…
Characterization and Mitigation of ADC Noise by Reference Tuning in RRAM-Based Compute-In-Memory
Ying-Hao Wei, Zishen Wan, Brian Crafton +2
With the escalating demand for power-efficient neural network architectures, non-volatile compute-in-memory designs have garnered significant attention. However, owing to the natur…
VAP: The Vulnerability-Adaptive Protection Paradigm Toward Reliable Autonomous Machines
Zishen Wan, Yiming Gan, Bo Yu +3
The next ubiquitous computing platform, following personal computers and smartphones, is poised to be inherently autonomous, encompassing technologies like drones, robots, and self…
Scaling Compute Is Not All You Need for Adversarial Robustness
Edoardo Debenedetti, Zishen Wan, Maksym Andriushchenko +3
The last six years have witnessed significant progress in adversarially robust deep learning. As evidenced by the CIFAR-10 dataset category in RobustBench benchmark, the accuracy u…
BERRY: Bit Error Robustness for Energy-Efficient Reinforcement Learning-Based Autonomous Systems
Zishen Wan, Nandhini Chandramoorthy, Karthik Swaminathan +3
Autonomous systems, such as Unmanned Aerial Vehicles (UAVs), are expected to run complex reinforcement learning (RL) models to execute fully autonomous position-navigation-time tas…
Real-Time Fully Unsupervised Domain Adaptation for Lane Detection in Autonomous Driving
Kshitij Bhardwaj, Zishen Wan, Arijit Raychowdhury +1
While deep neural networks are being utilized heavily for autonomous driving, they need to be adapted to new unseen environmental conditions for which they were not trained. We foc…
Robotic Computing on FPGAs: Current Progress, Research Challenges, and Opportunities
Zishen Wan, Ashwin Lele, Bo Yu +5
Robotic computing has reached a tipping point, with a myriad of robots (e.g., drones, self-driving cars, logistic robots) being widely applied in diverse scenarios. The continuous…
FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems
Zishen Wan, Aqeel Anwar, Abdulrahman Mahmoud +4
Swarm intelligence is being increasingly deployed in autonomous systems, such as drones and unmanned vehicles. Federated reinforcement learning (FRL), a key swarm intelligence para…
Analyzing and Improving Fault Tolerance of Learning-Based Navigation Systems
Zishen Wan, Aqeel Anwar, Yu-Shun Hsiao +3
Learning-based navigation systems are widely used in autonomous applications, such as robotics, unmanned vehicles and drones. Specialized hardware accelerators have been proposed f…
Roofline Model for UAVs:A Bottleneck Analysis Tool for Designing Compute Systems for Autonomous Drones
Srivatsan Krishnan, Zishen Wan, Kshitij Bhardwaj +2
We present a bottleneck analysis tool for designing compute systems for autonomous Unmanned Aerial Vehicles (UAV). The tool provides insights by exploiting the fundamental relation…
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning
Srivatsan Krishnan, Maximilian Lam, Sharad Chitlangia +4
Deep reinforcement learning continues to show tremendous potential in achieving task-level autonomy, however, its computational and energy demands remain prohibitively high. In thi…