A GPU Microarchitecture Optimized for Fully Homomorphic Encryption


Researchers from Boston University, Northeastern University, KAIST, and University of Murcia, et al. have released “FHECore: Rethinking GPU Microarchitecture for Fully Homomorphic Encryption”. Abstract“Fully Homomorphic Encryption (FHE) enables computation directly on encrypted data but incurs massive computational and memory overheads, often exceeding plaintext execution by seve... » read more

Study Of HW Acceleration for Neural Networks (Arizona State Univ.)


A new technical paper titled "Hardware Acceleration for Neural Networks: A Comprehensive Survey" was published by researchers at Arizona State University. Abstract "Neural networks have become a dominant computational workload across cloud and edge platforms, but their rapid growth in model size and deployment diversity has exposed hardware bottlenecks that are increasingly dominated by mem... » read more

Report: The AI Efficiency Boom


Artificial Intelligence (AI) is undergoing a fundamental transformation. While early AI models were large, compute-heavy, and dependent on cloud processing, a new wave of efficiency-driven innovations is moving AI inference—the generation of model results—to the edge. Smaller models, improved memory and compute performance, and the need for privacy, low latency, and energy efficiency are dr... » read more

AI Accelerators for Homomorphic Encryption Workloads


A new technical paper titled "Leveraging ASIC AI Chips for Homomorphic Encryption" was published by researchers at Georgia Tech, MIT, Google and Cornell University. Abstract: "Cloud-based services are making the outsourcing of sensitive client data increasingly common. Although homomorphic encryption (HE) offers strong privacy guarantee, it requires substantially more resources than compu... » read more

Characteristics and Potential HW Architectures for Neuro-Symbolic AI


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

Potentials And Issues Of Designing Fault-Tolerant Hardware Acceleration For Edge-Computing Devices


A technical paper titled “Fault-Tolerant Hardware Acceleration for High-Performance Edge-Computing Nodes” was published by researchers at University of Rome. Abstract: "High-performance embedded systems with powerful processors, specialized hardware accelerators, and advanced software techniques are all key technologies driving the growth of the IoT. By combining hardware and software tec... » read more

FPGA-Proven RISC-V System With Hardware Accelerated Task Scheduling


A technical paper titled “Enabling HW-based Task Scheduling in Large Multicore Architectures” was published by researchers at Barcelona Supercomputing Center, University of Campinas, University of Sao Paulo, and Arteris Inc. Abstract: "Dynamic Task Scheduling is an enticing programming model aiming to ease the development of parallel programs with intrinsically irregular or data-dependent... » read more

A Chiplet-Based FHE Accelerator Design Enabling Scalability And Higher Throughput


A technical paper titled “REED: Chiplet-Based Scalable Hardware Accelerator for Fully Homomorphic Encryption” was published by researchers at Graz University of Technology and Samsung Advanced Institute of Technology. Abstract: "Fully Homomorphic Encryption (FHE) has emerged as a promising technology for processing encrypted data without the need for decryption. Despite its potential, its... » read more

Advantages, Disadvantages, And Use Cases Of FPGAs


A technical paper titled “Data Processing with FPGAs on Modern Architectures” was published by researchers at ETH Zürich. Abstract: "Trends in hardware, the prevalence of the cloud, and the rise of highly demanding applications have ushered an era of specialization that is quickly changing the way data is processed at scale. These changes are likely to continue and accelerate in the next... » read more

Arbitrary Precision DNN Accelerator Controlled by a RISC-V CPU (Ecole Polytechnique Montreal, IBM, Mila, CMC)


A new technical paper titled "BARVINN: Arbitrary Precision DNN Accelerator Controlled by a RISC-V CPU" was written by researchers at Ecole Polytechnique Montreal, IBM, Mila and CMC Microsystems. It was accepted for publication in the 2023, 28th Asia and South Pacific Design Automation Conference (ASP-DAC 2023) in Japan. Abstract: "We present a DNN accelerator that allows inference at arbitr... » read more

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