200 citations · 244 across the 5 of their papers we have counts for
5 papers
Benchmarking TinyML Systems: Challenges and Direction
Colby R. Banbury, Vijay Janapa Reddi, Max Lam +14
Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to unlock an entirely new class of smart applications. However, continued progress is limited by…
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller
Bardienus P. Duisterhof, Srivatsan Krishnan, Jonathan J. Cruz +5
We present fully autonomous source seeking onboard a highly constrained nano quadcopter, by contributing application-specific system and observation feature design to enable infere…
Air Learning: A Deep Reinforcement Learning Gym for Autonomous Aerial Robot Visual Navigation
Srivatsan Krishnan, Behzad Boroujerdian, William Fu +2
We introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain rand…
Fully Homomorphic Image Processing
William Fu, Raymond Lin, Daniel Inge
Fully homomorphic encryption has allowed devices to outsource computation to third parties while preserving the secrecy of the data being computed on. Many images contain sensitive…
TaintAssembly: Taint-Based Information Flow Control Tracking for WebAssembly
William Fu, Raymond Lin, Daniel Inge
WebAssembly (wasm) has recently emerged as a promisingly portable, size-efficient, fast, and safe binary format for the web. As WebAssembly can interact freely with JavaScript libr…