no code implementations • 15 Jun 2023 • Matthew Stewart, Pete Warden, Yasmine Omri, Shvetank Prakash, Joao Santos, Shawn Hymel, Benjamin Brown, Jim MacArthur, Nat Jeffries, Sachin Katti, Brian Plancher, Vijay Janapa Reddi
Machine learning (ML) sensors are enabling intelligence at the edge by empowering end-users with greater control over their data.
no code implementations • 20 Dec 2022 • Evgenya Pergament, Pulkit Tandon, Oren Rippel, Lubomir Bourdev, Alexander G. Anderson, Bruno Olshausen, Tsachy Weissman, Sachin Katti, Kedar Tatwawadi
The contributions of this work are threefold: (1) we introduce a web-tool which allows scalable collection of fine-grained perceptual importance, by having users interactively paint spatio-temporal maps over encoded videos; (2) we use this tool to collect a dataset with 178 videos with a total of 14443 frames of human annotated spatio-temporal importance maps over the videos; and (3) we use our curated dataset to train a lightweight machine learning model which can predict these spatio-temporal importance regions.
1 code implementation • 7 Jun 2022 • Pete Warden, Matthew Stewart, Brian Plancher, Colby Banbury, Shvetank Prakash, Emma Chen, Zain Asgar, Sachin Katti, Vijay Janapa Reddi
Machine learning sensors represent a paradigm shift for the future of embedded machine learning applications.
1 code implementation • 8 May 2022 • Evgenya Pergament, Pulkit Tandon, Kedar Tatwawadi, Oren Rippel, Lubomir Bourdev, Bruno Olshausen, Tsachy Weissman, Sachin Katti, Alexander G. Anderson
We use this tool to collect data in-the-wild (10 videos, 17 users) and utilize the obtained importance maps in the context of x264 coding to demonstrate that the tool can indeed be used to generate videos which, at the same bitrate, look perceptually better through a subjective study - and are 1. 9 times more likely to be preferred by viewers.
no code implementations • 2 Dec 2021 • Manabu Nakanoya, Junha Im, Hang Qiu, Sachin Katti, Marco Pavone, Sandeep Chinchali
Autonomous vehicles (AVs) must interact with a diverse set of human drivers in heterogeneous geographic areas.
no code implementations • 8 Nov 2021 • Hang Qiu, Ioanna Vavelidou, Jian Li, Evgenya Pergament, Pete Warden, Sandeep Chinchali, Zain Asgar, Sachin Katti
The key challenge is that there is not much visibility into ML inference execution on edge devices, and very little awareness of potential issues during the edge deployment process.
1 code implementation • NeurIPS 2021 • Jiangnan Cheng, Marco Pavone, Sachin Katti, Sandeep Chinchali, Ao Tang
Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can improve independent control applications ranging from traffic scheduling to power generation.
no code implementations • 12 Dec 2020 • Sandeep Chinchali, Evgenya Pergament, Manabu Nakanoya, Eyal Cidon, Edward Zhang, Dinesh Bharadia, Marco Pavone, Sachin Katti
Today's robotic fleets are increasingly measuring high-volume video and LIDAR sensory streams, which can be mined for valuable training data, such as rare scenes of road construction sites, to steadily improve robotic perception models.
no code implementations • 6 Nov 2020 • Manabu Nakanoya, Sandeep Chinchali, Alexandros Anemogiannis, Akul Datta, Sachin Katti, Marco Pavone
However, today's representations for sensory data are mostly designed for human, not robotic, perception and thus often waste precious compute or wireless network resources to transmit unimportant parts of a scene that are unnecessary for a high-level robotic task.
no code implementations • 18 Oct 2020 • Eyal Cidon, Evgenya Pergament, Zain Asgar, Asaf Cidon, Sachin Katti
We characterize different sources for instability, and show that differences in compression formats and image signal processing account for significant instability in object classification models.
1 code implementation • ICLR 2020 • Tianshu Chu, Sandeep Chinchali, Sachin Katti
This paper considers multi-agent reinforcement learning (MARL) in networked system control.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 15 Feb 2019 • Sandeep Chinchali, Apoorva Sharma, James Harrison, Amine Elhafsi, Daniel Kang, Evgenya Pergament, Eyal Cidon, Sachin Katti, Marco Pavone
In this paper, we formulate a novel Robot Offloading Problem --- how and when should robots offload sensing tasks, especially if they are uncertain, to improve accuracy while minimizing the cost of cloud communication?
no code implementations • 13 Jan 2019 • Manikanta Kotaru, Guy Satat, Ramesh Raskar, Sachin Katti
In the context of imaging, RF spectrum holds many advantages compared to visible light systems.
no code implementations • 14 Nov 2018 • Assaf Eisenman, Maxim Naumov, Darryl Gardner, Misha Smelyanskiy, Sergey Pupyrev, Kim Hazelwood, Asaf Cidon, Sachin Katti
Typical large-scale recommender systems use deep learning models that are stored on a large amount of DRAM.
no code implementations • CVPR 2017 • Manikanta Kotaru, Sachin Katti
Today, experiencing virtual reality (VR) is a cumbersome experience which either requires dedicated infrastructure like infrared cameras to track the headset and hand-motion controllers (e. g., Oculus Rift, HTC Vive), or provides only 3-DoF (Degrees of Freedom) tracking which severely limits the user experience (e. g., Samsung Gear).