28nm-HKMG-Based FeFET Devices For Synaptic Applications

A technical paper titled "28 nm high-k-metal gate ferroelectric field effect transistors based synapses- A comprehensive overview" was published by researchers at Fraunhofer-Institut für Photonische Mikrosysteme IPMS, Indian Institute of Technology Madras, and GlobalFoundries. Abstract This invited article we present a comprehensive overview of 28 nm high-k-metal gate-based ferroelectric f... » read more

Autonomous Driving: End-to-End Surround 3D Camera Perception System (NVIDIA)

A new technical paper titled "NVAutoNet: Fast and Accurate 360∘ 3D Visual Perception For Self Driving" was published by researchers at NVIDIA. Abstract "Robust real-time perception of 3D world is essential to the autonomous vehicle. We introduce an end-to-end surround camera perception system for self-driving. Our perception system is a novel multi-task, multi-camera network which takes a... » read more

HW-SW Co-Design Solution For Building Side-Channel-Protected ML Hardware

A technical paper titled "Hardware-Software Co-design for Side-Channel Protected Neural Network Inference" was published (preprint) by researchers at North Carolina State University and Intel. Abstract "Physical side-channel attacks are a major threat to stealing confidential data from devices. There has been a recent surge in such attacks on edge machine learning (ML) hardware to extract the... » read more

Will Floating Point 8 Solve AI/ML Overhead?

While the media buzzes about the Turing Test-busting results of ChatGPT, engineers are focused on the hardware challenges of running large language models and other deep learning networks. High on the ML punch list is how to run models more efficiently using less power, especially in critical applications like self-driving vehicles where latency becomes a matter of life or death. AI already ... » read more

L-FinFET Neuron For A Highly Scalable Capacitive Neural Network (KAIST)

A new technical paper titled "An Artificial Neuron with a Leaky Fin-Shaped Field-Effect Transistor for a Highly Scalable Capacitive Neural Network" was published by researchers at KAIST (Korea Advanced Institute of Science and Technology). “In commercialized flash memory, tunnelling oxide prevents the trapped charges from escaping for better memory ability. In our proposed FinFET neuron, t... » read more

More Efficient Matrix-Multiplication Algorithms with Reinforcement Learning (DeepMind)

A new research paper titled "Discovering faster matrix multiplication algorithms with reinforcement learning" was published by researchers at DeepMind. "Here we report a deep reinforcement learning approach based on AlphaZero for discovering efficient and provably correct algorithms for the multiplication of arbitrary matrices," states the paper. Find the technical paper link here. Publis... » read more

Research Bits: June 14

Photonic deep neural network chip Engineers from the University of Pennsylvania built a photonic deep neural network on a 9.3 square millimeter chip they say is faster and more efficient at classifying images, with the ability to process nearly two billion images a second. The chip uses a series of waveguides that form 'neutron layers' mimicking the brain. “Our chip processes information ... » read more

Analog Edge Inference with ReRAM

Abstract "As the demands of big data applications and deep learning continue to rise, the industry is increasingly looking to artificial intelligence (AI) accelerators. Analog in-memory computing (AiMC) with emerging nonvolatile devices enable good hardware solutions, due to its high energy efficiency in accelerating the multiply-and-accumulation (MAC) operation. Herein, an Applied Materials... » read more

Exploring far-from-equilibrium ultrafast polarization control in ferroelectric oxides with excited-state neural network quantum molecular dynamics

New academic paper out of USC Viterbi School of Engineering: Abstract "Ferroelectric materials exhibit a rich range of complex polar topologies, but their study under far-from-equilibrium optical excitation has been largely unexplored because of the difficulty in modeling the multiple spatiotemporal scales involved quantum-mechanically. To study optical excitation at spatiotemporal scales w... » read more

A Framework For Ultra Low-Power Hardware Accelerators Using NNs For Embedded Time Series Classification

In embedded applications that use neural networks (NNs) for classification tasks, it is important to not only minimize the power consumption of the NN calculation, but of the whole system. Optimization approaches for individual parts exist, such as quantization of the NN or analog calculation of arithmetic operations. However, there is no holistic approach for a complete embedded system design ... » read more

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