Energy-Efficient DRAM↔PIM Transfers for PIM Systems (KAIST)


A new technical paper titled "PIM-MMU: A Memory Management Unit for Accelerating Data Transfers in Commercial PIM Systems" was published by researchers at KAIST. Abstract "Processing-in-memory (PIM) has emerged as a promising solution for accelerating memory-intensive workloads as they provide high memory bandwidth to the processing units. This approach has drawn attention not only from the... » read more

Analog In-Memory Computing: Fast Deep NN Training (IBM Research)


A new technical paper titled "Fast and robust analog in-memory deep neural network training" was published by researchers at IBM Research. Abstract "Analog in-memory computing is a promising future technology for efficiently accelerating deep learning networks. While using in-memory computing to accelerate the inference phase has been studied extensively, accelerating the training phase has... » read more

Scalable Chiplet System for LLM Training, Finetuning and Reduced DRAM Accesses (Tsinghua University)


A new technical paper titled "Hecaton: Training and Finetuning Large Language Models with Scalable Chiplet Systems" was published by researchers at Tsinghua University. Abstract "Large Language Models (LLMs) have achieved remarkable success in various fields, but their training and finetuning require massive computation and memory, necessitating parallelism which introduces heavy communicat... » read more

Hardware Security: One-Key Premise of Logic Locking


A new technical paper titled "Late Breaking Results: On the One-Key Premise of Logic Locking" was published by researchers at Synopsys. Abstract "The evaluation of logic locking methods has long been predicated on an implicit assumption that only the correct key can unveil the true functionality of a protected circuit. Consequently, a locking technique is deemed secure if it resists a good ... » read more

DL Compiler for Efficiently Utilizing Inter-Core Connected AI Chips (UIUC, Microsoft)


A new technical paper titled "Scaling Deep Learning Computation over the Inter-Core Connected Intelligence Processor" was published by researchers at UIUC and Microsoft Research. Abstract "As AI chips incorporate numerous parallelized cores to scale deep learning (DL) computing, inter-core communication is enabled recently by employing high-bandwidth and low-latency interconnect links on th... » read more

GPU Microarchitecture Integrating Dedicated Matrix Units At The Cluster Level (UC Berkeley)


A new technical paper titled "Virgo: Cluster-level Matrix Unit Integration in GPUs for Scalability and Energy Efficiency" was published by UC Berkeley. Abstract "Modern GPUs incorporate specialized matrix units such as Tensor Cores to accelerate GEMM operations central to deep learning workloads. However, existing matrix unit designs are tightly coupled to the SIMT core, limiting the size a... » read more

Benefits Of The Ultra-Low Leakage Currents from IGZO TFTs For Neuromorphic Applications


A new technical paper titled "A tunable multi-timescale Indium-Gallium-Zinc-Oxide thin-film transistor neuron towards hybrid solutions for spiking neuromorphic applications" was published by researchers at imec, CSIC Universidad de Sevilla, and Sungkyunkwan University. Abstract "Spiking neural network algorithms require fine-tuned neuromorphic hardware to increase their effectiveness. Such ... » read more

LLMs In The High-Level Synthesis Design Flow


A new technical paper titled "Are LLMs Any Good for High-Level Synthesis?" was published by researchers at University of Arizona. Abstract "The increasing complexity and demand for faster, energy-efficient hardware designs necessitate innovative High-Level Synthesis (HLS) methodologies. This paper explores the potential of Large Language Models (LLMs) to streamline or replace the HLS proces... » read more

Characterizing Three Supercomputers: Multi-GPU Interconnect Performance


A new technical paper titled "Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnects" was published by researchers at Sapienza University of Rome, University of Trento, Vrije Universiteit Amsterdam, ETH Zurich, CINECA, University of Antwerp, IBM Research Europe, HPE Cray, and NVIDIA. Abstract "Multi-GPU nodes are increasingly common in the rapidly evolving landscape... » read more

The Impact Of Simulation On The Carbon Footprint of Wafer Fab Equipment R&D


A new technical paper titled "Achieving Sustainability in the Semiconductor Industry: The Impact of Simulation and AI" was published by researchers at Lam Research. Abstract "Computational simulation has been used in the semiconductor industry since the 1950s to provide engineers and managers with a faster, more cost-effective method of designing semiconductors. With increased pressure in t... » read more

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