ReRAM-based in-memory computing (IMC) architectures enable efficient neural-network inference on edge devices, due to their high density and non-volatility. However, ReRAM is prone to the stuck-at faults (SAFs) which would distort weight mappings and reduce model accuracy substantially. To tackle this problem, a high-e...
Junlong Lin, Wenhua Wu, Mingtao Wang et al.· IEEE Transactions on Circuit...· 0 citations
Serving as catalysts for real-time insight, predictive intelligence, and autonomous decision-making, digital twin (DT) systems are driving transformation in industrial domains. In global logistics supply chains, the coupling of DTs with AI/ML, edge computing, and 5G/6G connectivity offers powerful capabilities for mark...
Static random-access memory (SRAM)-based computing-in-memory (CIM) macros have been widely studied to improve the energy efficiency of edge artificial intelligence (AI) inference tasks. However, less attention has been given to AI training, which requires CIM macros to not only perform matrix multiply-accumulate (MAC)...
Yiyang Yuan, Bingxin Zhang, Yiming Yang et al.· IEEE Journal of Solid-State...· 0 citations
We present PROTEUS, an 18 mm2 programmable general-purpose digital compute-in-memory (GP-DCIM) accelerator integrating 4 Mb resistive random access memory (RRAM) and 2.6 Mb tensor static random access memory (SRAM) with a 32-bit hierarchical DCIM instruction set architecture (ISA). PROTEUS features fine-grained 1-D mat...
Luqi Zheng, A. M. Bavani, Mufeng Chen et al.· IEEE Journal of Solid-State...· 0 citations
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Piecewise linear approximation (PLA) is pivotal for compressing time-series data under stringent error constraints. However, conventional PLA methods often struggle to reconcile the inherent conflict between aggressive compression ratios (CRs) and the preservation of complex local trends. In this article, we propose Tc...
Dong Chen, Wei-Jie Wang, Xianyou Zhu et al.· IEEE Internet of Things Jour...· 0 citations
This article investigates the dynamic multiobjective co-optimization problem in unmanned aerial vehicle (UAV)-assisted remote sensing systems, aiming to jointly optimize UAV placement, task scheduling strategies, and computing/communication resource allocation to minimize the system’s average processing latency and the...
Bo Wang, Xiaoyun Qin, Zhifeng Zhang et al.· IEEE Internet of Things Jour...· 0 citations
Vehicular edge computing (VEC), a key enabler for the Internet of Things (IoT) in intelligent transportation, addresses onboard processing constraints through collaborative task offloading among vehicles, facilitating latency-sensitive applications such as autonomous driving. However, developing efficient offloading st...
Momiao Zhou, Yimin Zhou, Yanshi Sun et al.· IEEE Internet of Things Jour...· 0 citations
Edge devices capable of running artificial intelligence (AI) applications have seen a surge in demand for energy-efficient and high-throughput computation. In this study, a 22-nm edge–AI processor, incorporating an accelerator with error-free Booth-value-confined (BVC) multiprecision (MP) multiplier and near-memory com...
Quan Cheng, Longyang Lin, Mingqiang Huang et al.· IEEE Transactions on Very La...· 0 citations
Graph convolutional networks (GCNs) have demonstrated strong capability in modeling graph-structured data and have been widely adopted in various industrial applications. With the rapid growth of edge computing, designing GCN accelerators that maintain high performance under strict resource constraints has become incre...
Yi-Zhang Wu, Xiang-Yin Chen, Chen Yang· IEEE Transactions on Very La...· 0 citations
Mobile edge computing (MEC) has emerged as a promising paradigm to support latency-sensitive applications by deploying computing and storage resources closer to end users. Service caching at the network edge is an effective approach to further reduce response latency and improve quality of service (QoS). However, the i...
Tianyang Zheng, Pengfei Yang, Kang Shen et al.· IEEE Internet of Things Jour...· 0 citations
Autonomous driving vehicles (ADVs) are transforming urban mobility with advanced sensors for real-time decision-making, promising safer and more efficient transportation. Despite recent advancements targeting accident reduction and efficiency improvement, challenges like sensor range limitations persist. Edge-assisted...
Zhe Jiang, Xiaoxuan Wang, Zhenghui Guo et al.· IEEE Transactions on Compute...· 0 citations
Multiaccess edge computing (MEC) has emerged as a key paradigm to empower resource-constrained edge devices through task offloading. However, dynamically fluctuating user preferences regarding latency and energy tradeoffs severely complicate the task scheduling and resource allocation, while existing methods struggle t...
Honggang Yuan, Yuxiang Deng, Xiao Du et al.· IEEE Internet of Things Jour...· 0 citations
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 28, 2026
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
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