20 citations · 52 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
AutoSoC: Automating Algorithm-SOC Co-design for Aerial Robots
Srivatsan Krishnan, Thierry Tambe, Zishen Wan +1
Aerial autonomous machines (Drones) has a plethora of promising applications and use cases. While the popularity of these autonomous machines continues to grow, there are many chal…
Widening Access to Applied Machine Learning with TinyML
Vijay Janapa Reddi, Brian Plancher, Susan Kennedy +21
Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloe…
RL-Scope: Cross-Stack Profiling for Deep Reinforcement Learning Workloads
James Gleeson, Srivatsan Krishnan, Moshe Gabel +3
Deep reinforcement learning (RL) has made groundbreaking advancements in robotics, data center management and other applications. Unfortunately, system-level bottlenecks in RL work…
AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs
Srivatsan Krishnan, Zishen Wan, Kshitij Bhardwaj +6
Building domain-specific accelerators for autonomous unmanned aerial vehicles (UAVs) is challenging due to a lack of systematic methodology for designing onboard compute. Balancing…