no code implementations • 1 Apr 2024 • Ninad Hogade, Sudeep Pasricha
This work introduces a unique approach combining Game Theory (GT) and Deep Reinforcement Learning (DRL) for optimizing the distribution of AI inference workloads in geo-distributed data centers to reduce carbon emissions and cloud operating (energy + data transfer) costs.
no code implementations • 7 Mar 2024 • Febin Sunny, Ebadollah Taheri, Mahdi Nikdast, Sudeep Pasricha
Modern machine learning (ML) applications are becoming increasingly complex and monolithic (single chip) accelerator architectures cannot keep up with their energy efficiency and throughput demands.
no code implementations • 3 Mar 2024 • Danish Gufran, Saideep Tiku, Sudeep Pasricha
Indoor localization is a critical task in many embedded applications, such as asset tracking, emergency response, and realtime navigation.
no code implementations • 12 Jan 2024 • Salma Afifi, Febin Sunny, Mahdi Nikdast, Sudeep Pasricha
In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) and graph processing have emerged as transformative technologies for natural language processing (NLP), computer vision, and graph-structured data applications.
1 code implementation • 16 Dec 2023 • Danish Gufran, Saideep Tiku, Sudeep Pasricha
However, the challenges of device heterogeneity and temporal variations have hindered its widespread adoption and accuracy.
no code implementations • 14 Nov 2023 • Sirui Qi, Dejan Milojicic, Cullen Bash, Sudeep Pasricha
To co-optimize the energy cost, carbon emissions, and water footprint of datacenter operation from a global perspective, we propose a novel framework for multi-objective sustainable datacenter management (MOSAIC) that integrates adaptive local search with a collaborative decomposition-based evolutionary algorithm to intelligently manage geographical workload distribution and datacenter operations.
no code implementations • 10 Nov 2023 • Danish Gufran, Sudeep Pasricha
Indoor localization has become increasingly vital for many applications from tracking assets to delivering personalized services.
no code implementations • 24 Aug 2023 • Sirui Qi, Dejan Milojicic, Cullen Bash, Sudeep Pasricha
Today's cloud data centers are often distributed geographically to provide robust data services.
no code implementations • 7 Aug 2023 • Amin Shafiee, Sanmitra Banerjee, Krishnendu Chakrabarty, Sudeep Pasricha, Mahdi Nikdast
The proposed models can be applied to any SP-NN architecture with different configurations to analyze the effect of loss and crosstalk.
no code implementations • 4 Jul 2023 • Salma Afifi, Febin Sunny, Amin Shafiee, Mahdi Nikdast, Sudeep Pasricha
Graph neural networks (GNNs) have emerged as a powerful approach for modelling and learning from graph-structured data.
1 code implementation • 4 Jul 2023 • Danish Gufran, Sudeep Pasricha
By deploying machine learning (ML) based indoor localization frameworks on their mobile devices, users can localize themselves in a variety of indoor and subterranean environments.
no code implementations • 22 Mar 2023 • Febin Sunny, Mahdi Nikdast, Sudeep Pasricha
Emerging AI applications such as ChatGPT, graph convolutional networks, and other deep neural networks require massive computational resources for training and inference.
no code implementations • 22 Mar 2023 • Salma Afifi, Febin Sunny, Mahdi Nikdast, Sudeep Pasricha
Transformer neural networks are rapidly being integrated into state-of-the-art solutions for natural language processing (NLP) and computer vision.
1 code implementation • 10 Mar 2023 • Sirui Qi, Yingheng Li, Sudeep Pasricha, Ryan Gary Kim
To enable emerging applications such as deep machine learning and graph processing, 3D network-on-chip (NoC) enabled heterogeneous manycore platforms that can integrate many processing elements (PEs) are needed.
no code implementations • 3 Mar 2023 • Abhishek Balasubramaniam, Febin P Sunny, Sudeep Pasricha
In this paper, we introduce a novel semi-structured pruning framework called R-TOSS that overcomes the shortcomings of state-of-the-art model pruning techniques.
no code implementations • 3 Mar 2023 • Christian Westbrook, Sudeep Pasricha
Machine learning (ML) algorithms are increasingly being integrated into embedded and IoT systems that surround us, and they are vulnerable to adversarial attacks.
no code implementations • 18 Feb 2023 • Danish Gufran, Saideep Tiku, Sudeep Pasricha
Wi-Fi fingerprinting-based indoor localization is an emerging embedded application domain that leverages existing Wi-Fi access points (APs) in buildings to localize users with smartphones.
no code implementations • 28 Jan 2023 • Febin Sunny, Ebadollah Taheri, Mahdi Nikdast, Sudeep Pasricha
Domain-specific machine learning (ML) accelerators such as Google's TPU and Apple's Neural Engine now dominate CPUs and GPUs for energy-efficient ML processing.
no code implementations • 23 Dec 2022 • Sudeep Pasricha, Marilyn Wolf
Computing systems are tightly integrated today into our professional, social, and private lives.
no code implementations • 2 Nov 2022 • Sudeep Pasricha
This article reviews the landscape of ethical challenges of integrating artificial intelligence (AI) into smart healthcare products, including medical electronic devices.
no code implementations • 21 Oct 2022 • Sudeep Pasricha
The dawn of the digital medicine era, ushered in by increasingly powerful embedded systems and Internet of Things (IoT) computing devices, is creating new therapies and biomedical solutions that promise to positively transform our quality of life.
no code implementations • 31 Aug 2022 • Febin Sunny, Mahdi Nikdast, Sudeep Pasricha
Recurrent Neural Networks (RNNs) are used in applications that learn dependencies in data sequences, such as speech recognition, human activity recognition, and anomaly detection.
no code implementations • 26 May 2022 • Kamil Khan, Sudeep Pasricha, Ryan Gary Kim
Network-on-chip (NoC) architectures rely on buffers to store flits to cope with contention for router resources during packet switching.
no code implementations • 17 May 2022 • Febin Sunny, Mahdi Nikdast, Sudeep Pasricha
Parameter quantization in convolutional neural networks (CNNs) can help generate efficient models with lower memory footprint and computational complexity.
no code implementations • 17 May 2022 • Liping Wang, Sudeep Pasricha
Modern indoor localization techniques are essential to overcome the weak GPS coverage in indoor environments.
no code implementations • 17 May 2022 • Saideep Tiku, Danish Gufran, Sudeep Pasricha
One such critical challenge is device heterogeneity, i. e., the variation in the RSSI signal characteristics captured across different smartphone devices.
no code implementations • 17 May 2022 • Joydeep Dey, Sudeep Pasricha
In emerging automotive cyber-physical systems (CPS), accurate environmental perception is critical to achieving safety and performance goals.
no code implementations • 17 May 2022 • Ninad Hogade, Sudeep Pasricha
We examine the challenges and the issues in current research focused on ML for cloud management and explore strategies for addressing these issues.
no code implementations • 19 Apr 2022 • Asif Mirza, Amin Shafiee, Sanmitra Banerjee, Krishnendu Chakrabarty, Sudeep Pasricha, Mahdi Nikdast
Simulation results for two example SPNNs of different scales under realistic and correlated FPVs indicate that the optimized MZIs can improve the inferencing accuracy by up to 93. 95% for the MNIST handwritten digit dataset -- considered as an example in this paper -- which corresponds to a <0. 5% accuracy loss compared to the variation-free case.
no code implementations • 8 Apr 2022 • Amin Shafiee, Sanmitra Banerjee, Krishnendu Chakrabarty, Sudeep Pasricha, Mahdi Nikdast
Compared to electronic accelerators, integrated silicon-photonic neural networks (SP-NNs) promise higher speed and energy efficiency for emerging artificial-intelligence applications.
no code implementations • 19 Jan 2022 • Vipin Kumar Kukkala, Sooryaa Vignesh Thiruloga, Sudeep Pasricha
Autonomous vehicles are on the horizon and will be transforming transportation safety and comfort.
no code implementations • 19 Jan 2022 • Abhishek Balasubramaniam, Sudeep Pasricha
Object detection is a computer vision task that has become an integral part of many consumer applications today such as surveillance and security systems, mobile text recognition, and diagnosing diseases from MRI/CT scans.
no code implementations • 14 Dec 2021 • Sanmitra Banerjee, Mahdi Nikdast, Sudeep Pasricha, Krishnendu Chakrabarty
Singular-value-decomposition-based coherent integrated photonic neural networks (SC-IPNNs) have a large footprint, suffer from high static power consumption for training and inference, and cannot be pruned using conventional DNN pruning techniques.
no code implementations • 11 Dec 2021 • Sanmitra Banerjee, Mahdi Nikdast, Sudeep Pasricha, Krishnendu Chakrabarty
We propose a novel hardware-aware magnitude pruning technique for coherent photonic neural networks.
no code implementations • 28 Nov 2021 • Saideep Tiku, Sudeep Pasricha
These factors are often ignored in indoor localization frameworks and cause gradual and catastrophic degradation of localization accuracy post-deployment (over weeks and months).
no code implementations • 9 Sep 2021 • Febin Sunny, Mahdi Nikdast, Sudeep Pasricha
Sparse neural networks can greatly facilitate the deployment of neural networks on resource-constrained platforms as they offer compact model sizes while retaining inference accuracy.
no code implementations • 12 Jul 2021 • Vipin K. Kukkala, Sooryaa V. Thiruloga, Sudeep Pasricha
Our proposed LATTE framework uses a stacked Long Short Term Memory (LSTM) predictor network with novel attention mechanisms to learn the normal operating behavior at design time.
no code implementations • 12 Jul 2021 • Febin P. Sunny, Asif Mirza, Mahdi Nikdast, Sudeep Pasricha
However, mapping sophisticated neural network models on these accelerators still entails significant energy and memory consumption, along with high inference time overhead.
no code implementations • 2 Jul 2021 • Liping Wang, Saideep Tiku, Sudeep Pasricha
GPS technology has revolutionized the way we localize and navigate outdoors.
no code implementations • 15 Apr 2021 • Saideep Tiku, Prathmesh Kale, Sudeep Pasricha
Indoor localization services are a crucial aspect for the realization of smart cyber-physical systems within cities of the future.
no code implementations • 13 Feb 2021 • Febin Sunny, Asif Mirza, Mahdi Nikdast, Sudeep Pasricha
Domain-specific neural network accelerators have seen growing interest in recent years due to their improved energy efficiency and inference performance compared to CPUs and GPUs.
no code implementations • 21 Sep 2020 • Kamil Khan, Sudeep Pasricha, Ryan Gary Kim
Due to amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become the bottleneck.
Hardware Architecture