category: Architectures

category: IoT, Security & Automotive

Flexible IGZO RISC-V Microprocessor
By Technical Paper Link - 26 Sep, 2024 - Comments: 0

A new technical paper titled "Bendable non-silicon RISC-V microprocessor" was published by researchers at Pragmatic Semiconductor, Qamcom,  and Harvard University. From the abstract: "Here we present Flex-RV, a 32-bit microprocessor based on an open RISC-V instruction set fabricated with indium gallium zinc oxide thin-film transistors on a flexible polyimide substrate, enabling an ultralow... » read more

Characteristics and Potential HW Architectures for Neuro-Symbolic AI
By Technical Paper Link - 23 Sep, 2024 - Comments: 0

A new technical paper titled "Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecture" was published by researchers at Georgia Tech, UC Berkeley, and IBM Research. Abstract: "The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational trajectories, li... » read more

Mixed Signal In-Memory Computing With Massively Parallel Gradient Calculations of High-Degree Polynomials
By Technical Paper Link - 19 Sep, 2024 - Comments: 0

A new technical paper titled "Computing high-degree polynomial gradients in memory" was published by researchers at UCSB, HP Labs, Forschungszentrum Juelich GmbH, and RWTH Aachen University. Abstract "Specialized function gradient computing hardware could greatly improve the performance of state-of-the-art optimization algorithms. Prior work on such hardware, performed in the context of Isi... » read more

Energy-Efficient DRAM↔PIM Transfers for PIM Systems (KAIST)
By Technical Paper Link - 18 Sep, 2024 - Comments: 0

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)
By Technical Paper Link - 10 Sep, 2024 - Comments: 0

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)
By Technical Paper Link - 04 Sep, 2024 - Comments: 0

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

DL Compiler for Efficiently Utilizing Inter-Core Connected AI Chips (UIUC, Microsoft)
By Technical Paper Link - 28 Aug, 2024 - Comments: 0

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)
By Technical Paper Link - 27 Aug, 2024 - Comments: 0

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
By Technical Paper Link - 27 Aug, 2024 - Comments: 0

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

Characterizing Three Supercomputers: Multi-GPU Interconnect Performance
By Technical Paper Link - 27 Aug, 2024 - Comments: 0

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

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