Hardware Trojan Attack For SNNs (Sorbonne Université, CNRS)


A new technical paper titled "Input-Triggered Hardware Trojan Attack on Spiking Neural Networks" was published by researchers at Sorbonne Universite, CNRS and Queen’s University Belfast. Abstract "Neuromorphic computing based on spiking neural networks (SNNs) is emerging as a promising alternative to traditional artificial neural networks (ANNs), offering unique advantages in terms of low... » read more

Inference Framework For Deployment Challenges of Large Generative Models On GPUs (Google)


A new technical paper titled "Scaling On-Device GPU Inference for Large Generative Models" was published by researchers at Google and Meta Platforms. Abstract "Driven by the advancements in generative AI, large machine learning models have revolutionized domains such as image processing, audio synthesis, and speech recognition. While server-based deployments remain the locus of peak perform... » read more

Optimizing End-to-End Communication And Workload Partitioning In MCM Accelerators (Georgia Tech)


A new technical paper titled "MCMComm: Hardware-Software Co-Optimization for End-to-End Communication in Multi-Chip-Modules" was published by researchers at Georgia Tech. Abstract "Increasing AI computing demands and slowing transistor scaling have led to the advent of Multi-Chip-Module (MCMs) based accelerators. MCMs enable cost-effective scalability, higher yield, and modular reuse by par... » read more

A Survey Of Digital Twins and Other Prototyping Technologies for Vehicles


A new technical paper titled "Digital Twin Technologies for Vehicular Prototyping: A Survey" was published by researchers at Central Michigan University and University of Florida. Abstract "Digital Twin (DT) technology is widely regarded as one of the most promising tools for industry development, demonstrating substantial application across numerous cyber-physical systems. Gradually, this ... » read more

HW-based Heterogeneous Memory Management for LLM Inferencing (KAIST, Stanford Unversity)


A new technical paper titled "Hardware-based Heterogeneous Memory Management for Large Language Model Inference" was published by researchers at KAIST and Stanford University. Abstract "A large language model (LLM) is one of the most important emerging machine learning applications nowadays. However, due to its huge model size and runtime increase of the memory footprint, LLM inferences suf... » read more

Single Transistor Memory Cell C2RAM Based On FDSOI For Quantum And Neuromorphic


A new technical paper titled "An Energy Efficient Memory Cell for Quantum and Neuromorphic Computing at Low Temperatures" was published by researchers at Forschungszentrum Jülich, RWTH Aachen University and SOITEC. Abstract: "Efficient computing in cryogenic environments, including classical von Neumann, quantum, and neuromorphic systems, is poised to transform big data processing. The que... » read more

ReRAM-Based, In-Memory Implementation Of Stochastic Computing


A new technical paper titled "All-in-Memory Stochastic Computing using ReRAM" was published by researchers at TU Dresden, Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Case Western Reserve University, University of Louisiana at Lafayette and Barkhausen Institut. Abstract "As the demand for efficient, low-power computing in embedded and edge devices grows, tradit... » read more

GenAI for Analog IC Design (McMaster University)


A new technical paper titled "Generative AI for Analog Integrated Circuit Design: Methodologies and Applications" was published by researchers at McMaster University. Abstract "Electronic Design Automation (EDA) in analog Integrated Circuits (ICs) has been the focus of extensive research; however, unlike its digital counterpart, it has not achieved widespread adoption. In this systematic re... » read more

Side-by-Side Benchmark of NPU Platforms (Imperial College London, Cambridge)


A new technical paper titled "Benchmarking Ultra-Low-Power μNPUs" was published by researchers at Imperial College London and University of Cambridge. Abstract "Efficient on-device neural network (NN) inference has various advantages over cloud-based processing, including predictable latency, enhanced privacy, greater reliability, and reduced operating costs for vendors. This has sparked t... » read more

LLM-based Agentic Framework Automating HW Security Threat Modeling And Test Plan Generation (U. of Florida)


A new technical paper titled "ThreatLens: LLM-guided Threat Modeling and Test Plan Generation for Hardware Security Verification" was published by researchers at University of Florida. Abstract "Current hardware security verification processes predominantly rely on manual threat modeling and test plan generation, which are labor-intensive, error-prone, and struggle to scale with increasing ... » read more

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