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

Using Silicon Photonics To Reduce Latency On Edge Devices


A new technical paper titled "Delocalized photonic deep learning on the internet’s edge" was published by researchers at MIT and Nokia Corporation. “Every time you want to run a neural network, you have to run the program, and how fast you can run the program depends on how fast you can pipe the program in from memory. Our pipe is massive — it corresponds to sending a full feature-leng... » read more

Active Learning to Reduce Data Requirements For Defect Identification in Semiconductor Manufacturing


A new technical paper titled "Exploring Active Learning for Semiconductor Defect Segmentation" was published by researchers at Agency for Science, Technology and Research (A*STAR) in Singapore. "We identify two unique challenges when applying AL on semiconductor XRM scans: large domain shift and severe class-imbalance. To address these challenges, we propose to perform contrastive pretrainin... » read more

Memory-Computation Decoupling Execution To Achieve Ideal All-Bank PIM Performance


A new technical paper titled "Achieving the Performance of All-Bank In-DRAM PIM With Standard Memory Interface: Memory-Computation Decoupling" was published by researchers at Korea University. "This paper proposed the memory-computation decoupled PIM architecture to provide the performance comparable to the all-bank PIM while preserving the standard DRAM interface, i.e., DRAM commands, powe... » read more

Nonvolatile ECRAM With A Short-Circuit Retention Time Several Orders of Magnitude Higher Than Previously Shown


A new technical paper titled "Nonvolatile Electrochemical Random-Access Memory Under Short Circuit" was published by researchers at University of Michigan and Sandia National Laboratories. Abstract "Electrochemical random-access memory (ECRAM) is a recently developed and highly promising analog resistive memory element for in-memory computing. One longstanding challenge of ECRAM is attainin... » read more

Hardware Platform Based on 2D Memtransistors


A new technical paper titled "Hardware implementation of Bayesian network based on two-dimensional memtransistors" from researchers at Penn State University. "In this work, we demonstrate hardware implementation of a BN [Bayesian networks] using a monolithic memtransistor technology based on two-dimensional (2D) semiconductors such as monolayer MoS2. First, we experimentally demonstrate a lo... » read more

Training a ML model On An Intelligent Edge Device Using Less Than 256KB Memory


A new technical paper titled "On-Device Training Under 256KB Memory" was published by researchers at MIT and MIT-IBM Watson AI Lab. “Our study enables IoT devices to not only perform inference but also continuously update the AI models to newly collected data, paving the way for lifelong on-device learning. The low resource utilization makes deep learning more accessible and can have a bro... » 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

Speeding-Up Thermal Simulations Of Chips With ML


A new technical paper titled "A Thermal Machine Learning Solver For Chip Simulation" was published by researchers at Ansys. Abstract "Thermal analysis provides deeper insights into electronic chips' behavior under different temperature scenarios and enables faster design exploration. However, obtaining detailed and accurate thermal profile on chip is very time-consuming using FEM or CFD. Th... » read more

Adaptive Memristive Hardware


A new technical paper titled "Self-organization of an inhomogeneous memristive hardware for sequence learning" was just published by researchers at University of Zurich, ETH Zurich, Université Grenoble Alpes, CEA, Leti and Toshiba. "We design and experimentally demonstrate an adaptive hardware architecture Memristive Self-organizing Spiking Recurrent Neural Network (MEMSORN). MEMSORN incorp... » read more

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