Award Abstract # 2216772
Collaborative Research: Integrated Sensing and Normally-off Computing for Edge Imaging Systems

NSF Org: ECCS
Division of Electrical, Communications and Cyber Systems
Recipient: NEW JERSEY INSTITUTE OF TECHNOLOGY
Initial Amendment Date: September 6, 2022
Latest Amendment Date: September 6, 2022
Award Number: 2216772
Award Instrument: Standard Grant
Program Manager: Ale Lukaszew
rlukasze@nsf.gov
 (703)292-8103
ECCS
 Division of Electrical, Communications and Cyber Systems
ENG
 Directorate for Engineering
Start Date: August 1, 2022
End Date: July 31, 2026 (Estimated)
Total Intended Award Amount: $279,334.00
Total Awarded Amount to Date: $279,334.00
Funds Obligated to Date: FY 2022 = $279,334.00
History of Investigator:
  • Shaahin Angizi (Principal Investigator)
    shaahin.angizi@njit.edu
Recipient Sponsored Research Office: New Jersey Institute of Technology
323 DR MARTIN LUTHER KING JR BLVD
NEWARK
NJ  US  07102-1824
(973)596-5275
Sponsor Congressional District: 10
Primary Place of Performance: New Jersey Institute of Technology
University Heights
Newark
NJ  US  07102-1982
Primary Place of Performance
Congressional District:
10
Unique Entity Identifier (UEI): SGBMHQ7VXNH5
Parent UEI:
NSF Program(s): CCSS-Comms Circuits & Sens Sys
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 756400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

Internet of Things (IoT) devices are projected to exceed $1000B by 2025, with a web of interconnection projected to comprise approximately 75+ billion IoT devices. The large number of IoTs consists of sensory imaging systems that enable massive data collection from the environment and people. However, considerable portions of the captured sensory data are redundant and unstructured. Data conversion of such large raw data, storing in volatile memories, transmission, and computation in on-/off-chip processors, impose high energy consumption, latency, and a memory bottleneck at the edge. Moreover, because renewing batteries for IoT devices is very costly and sometimes impracticable, energy harvesting devices with ambient energy sources and low maintenance have impacted a wide range of IoT applications such as wearable devices, smart cities, and the intelligent industry. This project explores and designs new high-speed, low-power, and normally-off computing architectures for resource-limited sensory nodes by exploiting cross-layer post-CMOS approaches to overcome these issues. Successful completion of this research will have benefits to a variety of critical application domains, including medical monitoring, industrial and/or environmental sensors. This project will make a strong effort on developing undergraduate and graduate course modules, propagating transportable and open-source models, and broadening STEM participation through publications/presentations at conferences for knowledge dissemination.

This project will follow two main research thrusts. Thrust 1 designs and analyzes a Processing-In-Sensor Unit (PISU) co-integrating always-on sensing and processing capabilities in conjunction with a Processing-Near-Sensor Unit (PNSU). The hybrid platform will feature real-time programmable granularity-configurable arithmetic operations to balance the accuracy, speed, and power-efficiency trade-offs under both continuous and energy-harvesting-powered imaging scenarios. This platform will enable resource-limited edge devices to locally perform data and compute-intensive applications such as machine learning tasks while consuming much less power than present state-of-the-art technology. The power profile of ambient energy sources imposes fundamental constraints on processing stability and duration. To achieve high sensing and computation parallelism under unstable power supply conditions, Intermittent-Robust Integrated Sensing Computation (IRISC) will be designed. During power failure, IRISC stores intermediate values in non-volatile spin-based devices, which will ensure uninterrupted operations. To meet the hardware constraints and mitigate the high write power of spin-based devices, they will be selectively and efficiently inserted within the datapaths through a novel NV-clustering methodology to create corresponding intermittent-robust IP cores that realize intermittent computation with lower power consumption while maintaining middleware coherence. This cross-layer devices-to-system research approach will be assessed by developing a comprehensive evaluation framework, a transportable energy-harvested computational workload suite, and FPGA-MRAM-based emulation platforms for IRISC.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 32)
Gaire, Rebati and Tabrizchi, Sepehr and Najafi, Deniz and Angizi, Shaahin and Roohi, Arman "DECO: Dynamic Energy-aware Compression and Optimization for In-Memory Neural Networks" , 2024 https://doi.org/10.1109/MWSCAS60917.2024.10658771 Citation Details
Morsali, Mehrdad and Tabrizchi, Sepehr and Liehr, Maximilian and Cady, Nathaniel and Imani, Mohsen and Roohi, Arman and Angizi, Shaahin "Deep Mapper: A Multi-Channel Single-Cycle Near-Sensor DNN Accelerator" , 2023 https://doi.org/10.1109/ICRC60800.2023.10386958 Citation Details
Morsali, Mehrdad and Tabrizchi, Sepehr and Marshall, Andrew and Roohi, Arman and Misra, Durga and Angizi, Shaahin "Design and Evaluation of a Near-Sensor Magneto-Electric FET-Based Event Detector" IEEE Transactions on Electron Devices , v.70 , 2023 https://doi.org/10.1109/TED.2023.3296389 Citation Details
Morsali, Mehrdad and Tabrizchi, Sepehr and Najafi, Deniz and Imani, Mohsen and Nikdast, Mahdi and Roohi, Arman and Angizi, Shaahin "OISA: Architecting an Optical In-Sensor Accelerator for Efficient Visual Computing" , 2024 https://doi.org/10.23919/DATE58400.2024.10546822 Citation Details
Morsali, Mehrdad and Tabrizchi, Sepehr and Velpula, Ravi Teja and Sankar_Muthu, Mano Bala and Trung_Nguyen, Hieu Pham and Imani, Mohsen and Roohi, Arman and Angizi, Shaahin "Energy-Efficient Near-Sensor Event Detector Based on Multilevel Ga 2 O 3 RRAM" , 2024 https://doi.org/10.1109/ISVLSI61997.2024.00067 Citation Details
Morsali, Mehrdad and Zhou, Ranyang and Tabrizchi, Sepehr and Roohi, Arman and Angizi, Shaahin "XOR-CiM: An Efficient Computing-in-SOT-MRAM Design for Binary Neural Network Acceleration" 2023 24th International Symposium on Quality Electronic Design (ISQED) , 2023 https://doi.org/10.1109/ISQED57927.2023.10129322 Citation Details
Najafi, Deniz and Barkam, Hamza Errahmouni and Morsali, Mehrdad and Jeong, SungHeon and Das, Tamoghno and Roohi, Arman and Nikdast, Mahdi and Imani, Mohsen and Angizi, Shaahin "Neuro-Photonix: Enabling Near-Sensor Neuro-Symbolic AI Computing on Silicon Photonics Substrate" IEEE Transactions on Circuits and Systems for Artificial Intelligence , 2025 https://doi.org/10.1109/TCASAI.2025.3537968 Citation Details
Najafi, Deniz and Morsali, Mehrdad and Zhou, Ranyang and Roohi, Arman and Marshall, Andrew and Misra, Durga and Angizi, Shaahin "Enabling Normally-Off In Situ Computing With a Magneto-Electric FET-Based SRAM Design" IEEE Transactions on Electron Devices , v.71 , 2024 https://doi.org/10.1109/TED.2024.3366172 Citation Details
Najafi, Deniz and Tabrizchi, Sepehr and Zhou, Ranyang and Amel_Solouki, Mohammadreza and Marshal, Andrew and Roohi, Arman and Angizi, Shaahin "Hybrid Magneto-electric FET-CMOS Integrated Memory Design for Instant-on Computing" , 2024 https://doi.org/10.1145/3649476.3660361 Citation Details
Reidy, Brendan and Tabrizchi, Sepehr and Mohammadi, Mohammadreza and Angizi, Shaahin and Roohi, Arman and Zand, Ramtin "HiRISE: High-Resolution Image Scaling for Edge ML via In-Sensor Compression and Selective ROI" , 2024 https://doi.org/10.1145/3649329.3656539 Citation Details
Roohi, Arman and Tabrizchi, Sepehr and Morsali, Mehrdad and Pan, David Z. and Angizi, Shaahin "PiPSim: A Behavior-Level Modeling Tool for CNN Processing-in-Pixel Accelerators" IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , v.43 , 2024 https://doi.org/10.1109/TCAD.2023.3305574 Citation Details
(Showing: 1 - 10 of 32)

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