2.4 A Distributed Autonomous and Collaborative Multi-Robot System Featuring a Low-Power Robot SoC in 22nm CMOS for Integrated Battery-Powered Minibots

Honkote, Vinayak; Kurian, Dileep; Muthukumar, Sriram; Ghosh, Dibyendu; Yada, Satish; Jain, Kartik; Jackson, Brad R.; Klotchkov, Ilya; Nimmagadda, Mallikarjuna Rao; Dattawadkar, Shreela; Deshmukh, Pranjali; Gupta, Ankit; Timbadiya, Jaykant; Pali, Ravi; Narayanan, K.; Soni, Saksham; Chhabra, Saransh; Dhama, Praveen; Sreenivasulu, Nese; Kollikunnel, Jisna; Kadavakollu, Sureshbabu; Sivaraj, Vijay Deepak; Aseron, Paolo; Azarenkov, Leonid; Robinson, Nancy M.; Radhakrishnan, Arun; Moiseev, Mikhail; Nandakumar, Ganeshram; Madhukumar, Akhila; Popov, Roman; Sahu, Kamakhya P.; Peguvandla, Ramesh; Ruiz, Alberto Del Rio; Bhartiya, Mukesh; Srinivasan, Anuradha; De, Vivek

doi:10.1109/isscc.2019.8662463

Cited by 11 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For end-to-end learning-based robotic control, Kim et al [14] develop a reinforcement learning accelerator for micro drones with 1.1 mW power consumption. For multi-robots scenarios, Honkote et al [15] design a low-power SoC for distributed and collaborative swarm robot systems. Different techniques ranging from voltage-mode circuits [16], quantized neural networks [17]- [19] and sparse coding [20] have been utilized in restricting the power consumption.…”

Section: Introductionmentioning

confidence: 99%

Circuit and System Technologies for Energy-Efficient Edge Robotics: (Invited Paper)

Wan

Lele

Raychowdhury

2022

2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)

View full text Add to dashboard Cite

As we march towards the age of ubiquitous intelligence, we note that AI and intelligence are progressively moving from the cloud to the edge. The success of Edge-AI is pivoted on innovative circuits and hardware that can enable inference and limited learning in resource-constrained edge autonomous systems. This paper introduces a series of ultra-low-power accelerator and system designs on enabling the intelligence in edge robotic platforms, including reinforcement learning neuromorphic control, swarm intelligence, and simultaneous mapping and localization. We put an emphasis on the impact of the mixedsignal circuit, neuro-inspired computing system, benchmarking and software infrastructure, as well as algorithm-hardware codesign to realize the most energy-efficient Edge-AI ASICs for the next-generation intelligent and autonomous systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Circuit and System Technologies for Energy-Efficient Edge Robotics: (Invited Paper)

Wan

Lele

Raychowdhury

2022

2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)

View full text Add to dashboard Cite

show abstract

“…In addition, the security robot [2] operates multiple robots at the same time to detect suspicious persons, detect abnormalities in the surrounding area, and report. In addition to these examples, there are autonomous traveling delivery robots [3] and systems in which multiple small robots work together to search and rescue victims and missing persons [4].…”

Section: Introductionmentioning

confidence: 99%

Fogcached-Ros: DRAM/NVMM Hybrid KVS Server with ROS Based Extension for ROS Application and SLAM Evaluation

Higashi

Ishiwata²,

Ohkawa

et al. 2021

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

“…Recently, several approaches have been proposed to accelerate motion planning on different hardware platforms, including GPUs [10], [29], FPGAs [6], [78], [93], and ASICs [66], [76], [95], [116]. Motion Planning Accelerators (MPAs) have achieved impressive performance gains and are being adopted in industry [46], [84], [89].…”

Section: Introductionmentioning

confidence: 99%

Characterizing and Improving the Resilience of Accelerators in Autonomous Robots

Shah¹,

Xue²,

Pattabiraman³

et al. 2021

Preprint

View full text Add to dashboard Cite

Motion planning is a computationally intensive and well-studied problem in autonomous robots. However, motion planning hardware accelerators (MPA) must be soft-error resilient for deployment in safety-critical applications, and blanket application of traditional mitigation techniques is ill-suited due to cost, power, and performance overheads. We propose Collision Exposure Factor (CEF), a novel metric to assess the failure vulnerability of circuits processing spatial relationships, including motion planning. CEF is based on the insight that the safety violation probability increases with the surface area of the physical space exposed by a bit-flip. We evaluate CEF on four MPAs. We demonstrate empirically that CEF is correlated with safety violation probability, and that CEF-aware selective error mitigation provides 12.3×, 9.6×, and 4.2× lower Failures-In-Time (FIT) rate on average for the same amount of protected memory compared to uniform, bit-position, and access-frequency-aware selection of critical data. Furthermore, we show how to employ CEF to enable fault characterization using 23, 000× fewer fault injection (FI) experiments than exhaustive FI, and evaluate our FI approach on different robots and MPAs. We demonstrate that CEF-aware FI can provide insights on vulnerable bits in an MPA while taking the same amount of time as uniform statistical FI. Finally, we use the CEF to formulate guidelines for designing soft-error resilient MPAs.

show abstract

2.4 A Distributed Autonomous and Collaborative Multi-Robot System Featuring a Low-Power Robot SoC in 22nm CMOS for Integrated Battery-Powered Minibots

Cited by 11 publications

References 3 publications

Circuit and System Technologies for Energy-Efficient Edge Robotics: (Invited Paper)

Circuit and System Technologies for Energy-Efficient Edge Robotics: (Invited Paper)

Fogcached-Ros: DRAM/NVMM Hybrid KVS Server with ROS Based Extension for ROS Application and SLAM Evaluation

Characterizing and Improving the Resilience of Accelerators in Autonomous Robots

Contact Info

Product

Resources

About