Efficient LLM Inference With Limited Memory (Apple)


A technical paper titled “LLM in a flash: Efficient Large Language Model Inference with Limited Memory” was published by researchers at Apple. Abstract: "Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks. However, their intensive computational and memory requirements present challenges, especially for device... » read more

8-In-1 Reconfigurable Logic Gate (TU Dresden)


A technical paper titled “The RGATE: an 8-in-1 Polymorphic Logic Gate Built from Reconfigurable Field Effect Transistors” was published by researchers at TU Dresden and NaMLab. Abstract: "We present the hardware implementation of a reconfigurable universal logic gate, that we call RGATE, able to deliver up to eight different logic functionalities and based on a symmetric four-transistors... » read more

Quantum Confinement And Its Effect On The Thermoelectric Performance For Thermal Management


A technical paper titled “Enhanced thermoelectric performance via quantum confinement in a metal oxide semiconductor field effect transistor for thermal management” was published by researchers at Sandia National Laboratories and Kansas State University. Abstract: "The performance of thermoelectric devices is gauged by the dimensionless figure of merit ZT. Improving ZT has proven to be a ... » read more

Environmentally Sustainable FPGAs (Notre Dame, Univ. of Pittsburgh)


A new technical paper titled "REFRESH FPGAs: Sustainable FPGA Chiplet Architectures" was published by University of Notre Dame and University of Pittsburgh. Abstract "There is a growing call for greater amounts of increasingly agile computational power for edge and cloud infrastructure to serve the computationally complex needs of ubiquitous computing devices. Thus, an important challenge i... » read more

SystemC-based Power Side-Channel Attacks Against AI Accelerators (Univ. of Lubeck)


A new technical paper titled "SystemC Model of Power Side-Channel Attacks Against AI Accelerators: Superstition or not?" was published by researchers at Germany's University of Lubeck. Abstract "As training artificial intelligence (AI) models is a lengthy and hence costly process, leakage of such a model's internal parameters is highly undesirable. In the case of AI accelerators, side-chann... » read more

Mixed SRAM And eDRAM Cell For Area And Energy-Efficient On-Chip AI Memory (Yale Univ.)


A new technical paper titled "MCAIMem: a Mixed SRAM and eDRAM Cell for Area and Energy-efficient on-chip AI Memory" was published by researchers at Yale University. Abstract: "AI chips commonly employ SRAM memory as buffers for their reliability and speed, which contribute to high performance. However, SRAM is expensive and demands significant area and energy consumption. Previous studies... » read more

Analog Planar Memristor Device: Developing, Designing, and Manufacturing


A new technical paper titled "Analog monolayer SWCNTs-based memristive 2D structure for energy-efficient deep learning in spiking neural networks" was published by researchers at Delft University of Technology and Khalifa University. Abstract: "Advances in materials science and memory devices work in tandem for the evolution of Artificial Intelligence systems. Energy-efficient computation... » read more

Forward Body Biasing in Bulk Cryo-CMOS With Negligible Leakage (TU Delft)


A new technical paper titled "Cryogenic-Aware Forward Body Biasing in Bulk CMOS" was published by researchers at QuTech, Tu Delft. Abstract "Cryogenic CMOS (cryo-CMOS) circuits are often hindered by the cryogenic threshold-voltage increase. To mitigate such an increase, a forward body biasing (FBB) technique in bulk CMOS is proposed, which can operate up to the nominal supply without prob... » read more

Hardware-Based Methodology To Protect AI Accelerators


A technical paper titled “A Unified Hardware-based Threat Detector for AI Accelerators” was published by researchers at Nanyang Technological University and Tsinghua University. Abstract: "The proliferation of AI technology gives rise to a variety of security threats, which significantly compromise the confidentiality and integrity of AI models and applications. Existing software-based so... » read more

A Survey Of Recent Advances In Spiking Neural Networks From Algorithms To HW Acceleration


A technical paper titled “Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technology” was published by researchers at Intel Labs, University of California Santa Cruz, University of Wisconsin-Madison, and University of Southern California. Abstract: "Neuromorphic computing and, in particular, spiking neural networks (SNNs) have becom... » read more

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