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Researcher profile

Sudeep Pasricha

Sudeep Pasricha contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Hardware ArchitectureMachine LearningArtificial IntelligenceEmerging TechnologiesCryptography and Securityeess.SPNeural and Evolutionary ComputingDistributed, Parallel, and Cluster ComputingComputer Visioncs.CYeess.SYphysics.opticsRoboticsSystems and Control

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Trust 21 - EmergingVerification L1Unclaimed author
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Published work

20 published item(s)

preprint2026arXiv

Autonomous Systems Dependability in the era of AI: Design Challenges in Safety, Security, Reliability and Certification

The design of embedded safety-critical systems such as those used in next-generation automotive and autonomous platforms, is increasingly challenged by escalating system complexity, hardware-software heterogeneity, and the integration of intelligent, data-driven components. Ensuring dependability in such systems requires a holistic approach that spans multiple abstraction layers and encompasses both design- and run-time assurance. Traditional methods for reliability, safety, and security management often fall short in addressing the dynamic and uncertain behaviors introduced by Artificial Intelligence (AI) and Machine Learning (ML) components, especially under stringent real-time, power, and safety constraints. While AI and ML offer powerful predictive, adaptive, and self-optimizing capabilities that can enhance system dependability, their inherent non-determinism, data-dependence, and lack of formal guarantees introduce new challenges for verification, validation, and certification. This paper explores emerging methodologies, architectures, and frameworks for designing dependable autonomous and embedded systems in the era of AI. It highlight advances in reliability modeling, secure system design, and certification approaches that account for imperfect, learning-enabled components, aiming to bridge the gap between AI innovation and certifiable system-level dependability.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks

Modern indoor localization techniques are essential to overcome the weak GPS coverage in indoor environments. Recently, considerable progress has been made in Channel State Information (CSI) based indoor localization with signal fingerprints. However, CSI signal patterns can be complicated in the large and highly dynamic indoor spaces with complex interiors, thus a solution for solving this issue is urgently needed to expand the applications of CSI to a broader indoor space. In this paper, we propose an end-to-end solution including data collection, pattern clustering, denoising, calibration and a lightweight one-dimensional convolutional neural network (1D CNN) model with CSI fingerprinting to tackle this problem. We have also created and plan to open source a CSI dataset with a large amount of data collected across complex indoor environments at Colorado State University. Experiments indicate that our approach achieves up to 68.5% improved performance (mean distance error) with minimal number of parameters, compared to the best-known deep machine learning and CSI-based indoor localization works.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization

Parameter quantization in convolutional neural networks (CNNs) can help generate efficient models with lower memory footprint and computational complexity. But, homogeneous quantization can result in significant degradation of CNN model accuracy. In contrast, heterogeneous quantization represents a promising approach to realize compact, quantized models with higher inference accuracies. In this paper, we propose HQNNA, a CNN accelerator based on non-coherent silicon photonics that can accelerate both homogeneously quantized and heterogeneously quantized CNN models. Our analyses show that HQNNA achieves up to 73.8x better energy-per-bit and 159.5x better throughput-energy efficiency than state-of-the-art photonic CNN accelerators.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management

Cloud workloads today are typically managed in a distributed environment and processed across geographically distributed data centers. Cloud service providers have been distributing data centers globally to reduce operating costs while also improving quality of service by using intelligent workload and resource management strategies. Such large scale and complex orchestration of software workload and hardware resources remains a difficult problem to solve efficiently. Researchers and practitioners have been trying to address this problem by proposing a variety of cloud management techniques. Mathematical optimization techniques have historically been used to address cloud management issues. But these techniques are difficult to scale to geo-distributed problem sizes and have limited applicability in dynamic heterogeneous system environments, forcing cloud service providers to explore intelligent data-driven and Machine Learning (ML) based alternatives. The characterization, prediction, control, and optimization of complex, heterogeneous, and ever-changing distributed cloud resources and workloads employing ML methodologies have received much attention in recent years. In this article, we review the state-of-the-art ML techniques for the cloud data center management problem. We examine the challenges and the issues in current research focused on ML for cloud management and explore strategies for addressing these issues. We also discuss advantages and disadvantages of ML techniques presented in the recent literature and make recommendations for future research directions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Electronic, Wireless, and Photonic Network-on-Chip Security: Challenges and Countermeasures

Networks-on-chips (NoCs) are an integral part of emerging manycore computing chips. They play a key role in facilitating communication among processing cores and between cores and memory. To meet the aggressive performance and energy-efficiency targets of machine learning and big data applications, NoCs have been evolving to leverage emerging paradigms such as silicon photonics and wireless communication. Increasingly, these NoC fabrics are becoming susceptible to security vulnerabilities, such as from hardware trojans that can snoop, corrupt, or disrupt information transfers on NoCs. This article surveys the landscape of security challenges and countermeasures across electronic, wireless, and photonic NoCs.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Embedded Systems Education in the 2020s: Challenges, Reflections, and Future Directions

Embedded computing systems are pervasive in our everyday lives, imparting digital intelligence to a variety of electronic platforms used in our vehicles, smart appliances, wearables, mobile devices, and computers. The need to train the next generation of embedded systems designers and engineers with relevant skills across hardware, software, and their co-design remains pressing today. This paper describes the evolution of embedded systems education over the past two decades and challenges facing the designers and instructors of embedded systems curricula in the 2020s. Reflections from over a decade of teaching the design of embedded computing systems are presented, with insights on strategies that show promise to address these challenges. Lastly, some important future directions in embedded systems education are highlighted.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in Integrated Silicon-Photonic Neural Networks

Compared to electronic accelerators, integrated silicon-photonic neural networks (SP-NNs) promise higher speed and energy efficiency for emerging artificial-intelligence applications. However, a hitherto overlooked problem in SP-NNs is that the underlying silicon photonic devices suffer from intrinsic optical loss and crosstalk noise, the impact of which accumulates as the network scales up. Leveraging precise device-level models, this paper presents the first comprehensive and systematic optical loss and crosstalk modeling framework for SP-NNs. For an SP-NN case study with two hidden layers and 1380 tunable parameters, we show a catastrophic 84% drop in inferencing accuracy due to optical loss and crosstalk noise.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization

Smartphones together with RSSI fingerprinting serve as an efficient approach for delivering a low-cost and high-accuracy indoor localization solution. However, a few critical challenges have prevented the wide-spread proliferation of this technology in the public domain. One such critical challenge is device heterogeneity, i.e., the variation in the RSSI signal characteristics captured across different smartphone devices. In the real-world, the smartphones or IoT devices used to capture RSSI fingerprints typically vary across users of an indoor localization service. Conventional indoor localization solutions may not be able to cope with device-induced variations which can degrade their localization accuracy. We propose a multi-head attention neural network-based indoor localization framework that is resilient to device heterogeneity. An in-depth analysis of our proposed framework across a variety of indoor environments demonstrates up to 35% accuracy improvement compared to state-of-the-art indoor localization techniques.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Object Detection in Autonomous Vehicles: Status and Open Challenges

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. Object detection is also one of the critical components to support autonomous driving. Autonomous vehicles rely on the perception of their surroundings to ensure safe and robust driving performance. This perception system uses object detection algorithms to accurately determine objects such as pedestrians, vehicles, traffic signs, and barriers in the vehicle's vicinity. Deep learning-based object detectors play a vital role in finding and localizing these objects in real-time. This article discusses the state-of-the-art in object detectors and open challenges for their integration into autonomous vehicles.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

RACE: A Reinforcement Learning Framework for Improved Adaptive Control of NoC Channel Buffers

Network-on-chip (NoC) architectures rely on buffers to store flits to cope with contention for router resources during packet switching. Recently, reversible multi-function channel (RMC) buffers have been proposed to simultaneously reduce power and enable adaptive NoC buffering between adjacent routers. While adaptive buffering can improve NoC performance by maximizing buffer utilization, controlling the RMC buffer allocations requires a congestion-aware, scalable, and proactive policy. In this work, we present RACE, a novel reinforcement learning (RL) framework that utilizes better awareness of network congestion and a new reward metric ("falsefulls") to help guide the RL agent towards better RMC buffer control decisions. We show that RACE reduces NoC latency by up to 48.9%, and energy consumption by up to 47.1% against state-of-the-art NoC buffer control policies.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonics

Recurrent Neural Networks (RNNs) are used in applications that learn dependencies in data sequences, such as speech recognition, human activity recognition, and anomaly detection. In recent years, newer RNN variants, such as GRUs and LSTMs, have been used for implementing these applications. As many of these applications are employed in real-time scenarios, accelerating RNN/LSTM/GRU inference is crucial. In this paper, we propose a novel photonic hardware accelerator called RecLight for accelerating simple RNNs, GRUs, and LSTMs. Simulation results indicate that RecLight achieves 37x lower energy-per-bit and 10% better throughput compared to the state-of-the-art.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ReSiPI: A Reconfigurable Silicon-Photonic 2.5D Chiplet Network with PCMs for Energy-Efficient Interposer Communication

2.5D chiplet systems have been proposed to improve the low manufacturing yield of large-scale chips. However, connecting the chiplets through an electronic interposer imposes a high traffic load on the interposer network. Silicon photonics technology has shown great promise towards handling a high volume of traffic with low latency in intra-chip network-on-chip (NoC) fabrics. Although recent advances in silicon photonic devices have extended photonic NoCs to enable high bandwidth communication in 2.5D chiplet systems, such interposer-based photonic networks still suffer from high power consumption. In this work, we design and analyze a novel Reconfigurable power-efficient and congestion-aware Silicon Photonic 2.5D Interposer network, called ReSiPI. Considering run-time traffic, ReSiPI is able to dynamically deploy inter-chiplet photonic gateways to improve the overall network congestion. ReSiPI also employs switching elements based on phase change materials (PCMs) to dynamically reconfigure and power-gate the photonic interposer network, thereby improving the network power efficiency. Compared to the best prior state-of-the-art 2.5D photonic network, ReSiPI demonstrates, on average, 37% lower latency, 25% power reduction, and 53% energy minimization in the network.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Roadmap for Cybersecurity in Autonomous Vehicles

Autonomous vehicles are on the horizon and will be transforming transportation safety and comfort. These vehicles will be connected to various external systems and utilize advanced embedded systems to perceive their environment and make intelligent decisions. However, this increased connectivity makes these vehicles vulnerable to various cyber-attacks that can have catastrophic effects. Attacks on automotive systems are already on the rise in today's vehicles and are expected to become more commonplace in future autonomous vehicles. Thus, there is a need to strengthen cybersecurity in future autonomous vehicles. In this article, we discuss major automotive cyber-attacks over the past decade and present state-of-the-art solutions that leverage artificial intelligence (AI). We propose a roadmap towards building secure autonomous vehicles and highlight key open challenges that need to be addressed.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Robust Perception Architecture Design for Automotive Cyber-Physical Systems

In emerging automotive cyber-physical systems (CPS), accurate environmental perception is critical to achieving safety and performance goals. Enabling robust perception for vehicles requires solving multiple complex problems related to sensor selection/ placement, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. We present PASTA, a novel framework for global co-optimization of deep learning and sensing for dependable vehicle perception. Experimental results with the Audi-TT and BMW-Minicooper vehicles show how PASTA can find robust, vehicle-specific perception architecture solutions.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A Survey on Silicon Photonics for Deep Learning

Deep learning has led to unprecedented successes in solving some very difficult problems in domains such as computer vision, natural language processing, and general pattern recognition. These achievements are the culmination of decades-long research into better training techniques and deeper neural network models, as well as improvements in hardware platforms that are used to train and execute the deep neural network models. Many application-specific integrated circuit (ASIC) hardware accelerators for deep learning have garnered interest in recent years due to their improved performance and energy-efficiency over conventional CPU and GPU architectures. However, these accelerators are constrained by fundamental bottlenecks due to 1) the slowdown in CMOS scaling, which has limited computational and performance-per-watt capabilities of emerging electronic processors, and 2) the use of metallic interconnects for data movement, which do not scale well and are a major cause of bandwidth, latency, and energy inefficiencies in almost every contemporary processor. Silicon photonics has emerged as a promising CMOS-compatible alternative to realize a new generation of deep learning accelerators that can use light for both communication and computation. This article surveys the landscape of silicon photonics to accelerate deep learning, with a coverage of developments across design abstractions in a bottom-up manner, to convey both the capabilities and limitations of the silicon photonics paradigm in the context of deep learning acceleration.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Accelerator

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. In this paper, we propose a novel cross-layer optimized neural network accelerator called CrossLight that leverages silicon photonics. CrossLight includes device-level engineering for resilience to process variations and thermal crosstalk, circuit-level tuning enhancements for inference latency reduction, and architecture-level optimization to enable higher resolution, better energy-efficiency, and improved throughput. On average, CrossLight offers 9.5x lower energy-per-bit and 15.9x higher performance-per-watt at 16-bit resolution than state-of-the-art photonic deep learning accelerators.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Survey of Resource Management for Processing-in-Memory and Near-Memory Processing Architectures

Due to amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become the bottleneck. Data-centric computing (DCC), as enabled by processing-in-memory (PIM) and near-memory processing (NMP) paradigms, aims to accelerate these types of applications by moving the computation closer to the data. Over the past few years, researchers have proposed various memory architectures that enable DCC systems, such as logic layers in 3D stacked memories or charge sharing based bitwise operations in DRAM. However, application-specific memory access patterns, power and thermal concerns, memory technology limitations, and inconsistent performance gains complicate the offloading of computation in DCC systems. Therefore, designing intelligent resource management techniques for computation offloading is vital for leveraging the potential offered by this new paradigm. In this article, we survey the major trends in managing PIM and NMP-based DCC systems and provide a review of the landscape of resource management techniques employed by system designers for such systems. Additionally, we discuss the future challenges and opportunities in DCC management.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Exploiting Process Variations to Secure Photonic NoC Architectures from Snooping Attacks

The compact size and high wavelength-selectivity of microring resonators (MRs) enable photonic networks-on-chip (PNoCs) to utilize dense-wavelength-division-multiplexing (DWDM) in their photonic waveguides, and as a result, attain high bandwidth on-chip data transfers. Unfortunately, a Hardware Trojan in a PNoC can manipulate the electrical driving circuit of its MRs to cause the MRs to snoop data from the neighboring wavelength channels in a shared photonic waveguide, which introduces a serious security threat. This paper presents a framework that utilizes process variation-based authentication signatures along with architecture-level enhancements to protect against data-snooping Hardware Trojans during unicast as well as multicast transfers in PNoCs. Evaluation results indicate that our framework can improve hardware security across various PNoC architectures with minimal overheads of up to 14.2% in average latency and of up to 14.6% in energy-delay-product (EDP).

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

INDRA: Intrusion Detection using Recurrent Autoencoders in Automotive Embedded Systems

Today's vehicles are complex distributed embedded systems that are increasingly being connected to various external systems. Unfortunately, this increased connectivity makes the vehicles vulnerable to security attacks that can be catastrophic. In this work, we present a novel Intrusion Detection System (IDS) called INDRA that utilizes a Gated Recurrent Unit (GRU) based recurrent autoencoder to detect anomalies in Controller Area Network (CAN) bus-based automotive embedded systems. We evaluate our proposed framework under different attack scenarios and also compare it with the best known prior works in this area.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

LORAX: Loss-Aware Approximations for Energy-Efficient Silicon Photonic Networks-on-Chip

The approximate computing paradigm advocates for relaxing accuracy goals in applications to improve energy-efficiency and performance. Recently, this paradigm has been explored to improve the energy efficiency of silicon photonic networks-on-chip (PNoCs). In this paper, we propose a novel framework (LORAX) to enable more aggressive approximation during communication over silicon photonic links in PNoCs. Given that silicon photonic interconnects have significant power dissipation due to the laser sources that generate the wavelengths for photonic communication, our framework attempts to reduce laser power overheads while intelligently approximating communication such that application output quality is not distorted beyond an acceptable limit. To the best of our knowledge, this is the first work that considers loss-aware laser power management and multilevel signaling to enable effective data approximation and energy-efficiency in PNoCs. Simulation results show that our framework can achieve up to 31.4% lower laser power consumption and up to 12.2% better energy efficiency than the best known prior work on approximate communication with silicon photonic interconnects, for the same application output quality

0 citations0 reviewsNo code linkedOpen access