A technical paper titled “Chiplets on Wheels: Review Paper on Holistic Chiplet Solutions for Autonomous Vehicles” was published by researchers at the Indian Institute of Technology, Madras.

Abstract
“On the advent of the slow death of Moore’s law, the silicon industry is moving towards a new era of chiplets. The automotive industry is experiencing a profound transformation towards software-defined vehicles, fueled by the surging demand for automotive compute chips, expected to reach 20-22 billion by 2030. High-performance compute (HPC) chips become instrumental in meeting the soaring demand for computational power. Various strategies, including centralized electrical and electronic architecture and the innovative Chiplet Systems, are under exploration. The latter, breaking down System-on-Chips (SoCs) into functional units, offers unparalleled customization and integration possibilities. The research accentuates the crucial open Chiplet ecosystem, fostering collaboration and enhancing supply chain resilience. In this paper, we address the unique challenges that arise when attempting to leverage chiplet-based architecture to design a holistic silicon solution for the automotive industry. We propose a throughput-oriented micro-architecture for ADAS and infotainment systems alongside a novel methodology to evaluate chiplet architectures. Further, we develop in-house simulation tools leveraging the gem5 framework to simulate latency and throughput. Finally, we perform an extensive design of thermally-aware chiplet placement and develop a micro-fluids-based cooling design.”

Find the technical paper here. Published May 2024.

Narashiman, Swathi, Divyaratna Joshi, Deepak Sridhar, Harish Rajesh, Sanjay Sattva, and Varun Manjunath. “Chiplets on Wheels: Review Paper on Holistic Chiplet Solutions for Autonomous Vehicles.” arXiv preprint arXiv:2406.00182 (2024).

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A technical paper titled “Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning” was published by researchers at University of Texas at Dallas, Intel Corporation, NXP Semiconductors, and Texas Instruments.

Abstract:

“Given the widespread use of safety-critical applications in the automotive field, it is crucial to ensure the Functional Safety (FuSa) of circuits and components within automotive systems. The Analog and Mixed-Signal (AMS) circuits prevalent in these systems are more vulnerable to faults induced by parametric perturbations, noise, environmental stress, and other factors, in comparison to their digital counterparts. However, their continuous signal characteristics present an opportunity for early anomaly detection, enabling the implementation of safety mechanisms to prevent system failure. To address this need, we propose a novel framework based on unsupervised machine learning for early anomaly detection in AMS circuits. The proposed approach involves injecting anomalies at various circuit locations and individual components to create a diverse and comprehensive anomaly dataset, followed by the extraction of features from the observed circuit signals. Subsequently, we employ clustering algorithms to facilitate anomaly detection. Finally, we propose a time series framework to enhance and expedite anomaly detection performance. Our approach encompasses a systematic analysis of anomaly abstraction at multiple levels pertaining to the automotive domain, from hardware- to block-level, where anomalies are injected to create diverse fault scenarios. By monitoring the system behavior under these anomalous conditions, we capture the propagation of anomalies and their effects at different abstraction levels, thereby potentially paving the way for the implementation of reliable safety mechanisms to ensure the FuSa of automotive SoCs. Our experimental findings indicate that our approach achieves 100% anomaly detection accuracy and significantly optimizes the associated latency by 5X, underscoring the effectiveness of our devised solution.”

Find the technical paper here. Published April 2024 (preprint).

Arunachalam, Ayush, Ian Kintz, Suvadeep Banerjee, Arnab Raha, Xiankun Jin, Fei Su, Viswanathan Pillai Prasanth, Rubin A. Parekhji, Suriyaprakash Natarajan, and Kanad Basu. “Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning.” arXiv preprint arXiv:2404.01632 (2024).

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The post Framework For Early Anomaly Detection In AMS Components Of Automotive SoCs appeared first on Semiconductor Engineering.