Scheduling Multi-Model AI Workloads On Heterogeneous MCM Accelerators (UC Irvine)

A technical paper titled “SCAR: Scheduling Multi-Model AI Workloads on Heterogeneous Multi-Chiplet Module Accelerators” was published by researchers at University of California Irvine. Abstract: "Emerging multi-model workloads with heavy models like recent large language models significantly increased the compute and memory demands on hardware. To address such increasing demands, designin... » read more

New Ways To Optimize GEMM-Based Applications Targeting Two Leading AI-Optimized FPGA Architectures

A technical paper titled “Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAs” was published by researchers at The University of Texas at Austin and Arizona State University. Abstract: "FPGAs are a promising platform for accelerating Deep Learning (DL) applications, due to their high performance, low power consumption, and reconfigurability. Recently, the leading FPGA... » read more

Embrace The New!

The ResNet family of machine learning algorithms was introduced to the AI world in 2015. A slew of variations was rapidly discovered that at the time pushed the accuracy of ResNets close to the 80% threshold (78.57% Top 1 accuracy for ResNet-152 on ImageNet). This state-of-the-art performance at the time, coupled with the rather simple operator structure that was readily amenable to hardware ac... » 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

Hardware Security for Silicon Photonic-Based AI Accelerators

A technical paper titled “Integrated Photonic AI Accelerators under Hardware Security Attacks: Impacts and Countermeasures” was published by researchers at Ecole Polytechnique de Montreal and Colorado State University. Abstract: "Integrated photonics based on silicon photonics platform is driving several application domains, from enabling ultra-fast chip-scale communication in high-perfor... » read more

(Vision) Transformers: Rise Of The Chimera

It’s 2023 and transformers are having a moment. No, I’m not talking about the latest installment of the Transformers movie franchise, "Transformers: Rise of the Beasts"; I’m talking about the deep learning model architecture class, transformers, that is fueling anticipation, excitement, fear, and investment in AI. Transformers are not so new in the world of AI anymore; they were first ... » read more

Chiplets: Bridging The Gap Between The System Requirements And Design Aggregation, Planning, And Optimization

A technical paper titled “System and Design Technology Co-optimization of Chiplet-based AI Accelerator with Machine Learning” was published by researchers at Auburn University. Abstract: "With the availability of advanced packaging technology and its attractive features, the chiplet-based architecture has gained traction among chip designers. The large design space and the lack of sys... » read more

Nightmare Fuel: The Hazards Of ML Hardware Accelerators

A major design challenge facing numerous silicon design teams in 2023 is building the right amount of machine learning (ML) performance capability into today’s silicon tape out in anticipation of what the state of the art (SOTA) ML inference models will look like in 2026 and beyond when that silicon will be used in devices in volume production. Given the continuing rapid rate of change in mac... » read more

Operator Anxiety

Are you one of the early pioneers who have purchased an electric car? In the United States in Q3 2022, 6% of new vehicle sales were pure electric models. Despite all the hype — and significant purchase subsidies in support of battery cars — today only 1% of the cumulative number of vehicles in service in the US are purely plug-in electric. One of the reasons electric car sales have not full... » read more

Scalable Optical AI Accelerator Based on a Crossbar Architecture

A new technical paper titled "Scalable Coherent Optical Crossbar Architecture using PCM for AI Acceleration" was published by researchers at University of Washington. Abstract: "Optical computing has been recently proposed as a new compute paradigm to meet the demands of future AI/ML workloads in datacenters and supercomputers. However, proposed implementations so far suffer from lack of sc... » read more

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