Search Results - computational+efficiency

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Event-Driven Integrate and Fire (EIF) Neuron Circuit for Neuromorphic Computing System (Case No. 2024-275)

Summary: Researchers in the UCLA Department of Electrical and Computer Engineering have developed an energy efficient neuromorphic computing architecture. Background: Widespread growth in demand for artificial intelligence systems has highlighted limitations in current central processing unit (CPU) designs, particularly in terms of energy efficiency...

Published: 3/4/2025 | Inventor(s): Mau-Chung Chang, Chao Jen Tien, Yong Hei

Keywords(s): Advanced Computing / AI, advanced computing methods, AI hardware, analog computing, Artifical Intelligence (Machine Learning, Data Mining), artificial electromagnetic materials, Artificial Intelligence, artificial intelligence algorithms, artificial intelligence augmentation, artificial intelligence/machine learning models, artificial intelligence-generated content, Artificial Neural Network, Artificial Neural Network Artificial Neuron, artificial presenting cells, artificial-intelligent materials, Cloud Computing, computational efficiency, computational imaging, compute-in-memory, Computer Aided Learning, Computer Architecture, Computer Monitor, Computer Vision, CPU design, deep neural networks (DNN), Energy Density, Energy Efficiency, event-driven processing, generative artificial intelligence, latency encoding, low latency computing, low-power architecture, matrix multiplication, Medical artificial intelligence (AI), Neuromorphic computing, offline learning, online learning, spike neural networks (SNN), Supercomputer

Category(s): Electrical, Electrical > Signal Processing, Electrical > Electronics & Semiconductors, Electrical > Computing Hardware, Software & Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning

Efficient Stochastic Compute-In-Memory Circuit for Multi-Level OR Accumulation (Case No. 2024-122)

Summary: UCLA researchers in the Department of Electrical and Computer Engineering have developed a novel computational hardware that combines stochastic computing with compute-in-memory techniques that improves speed and computational efficiency. Background: In recent years, the need for advanced computational technologies has surged, driven by...

Published: 2/14/2025 | Inventor(s): Sudhakar Pamarti, Jiyue Yang, Soumitra Pal, Puneet Gupta, Tianmu Li, Wojciech Romaszkan

Keywords(s): computational efficiency, Computer Architecture, Integrated Circuit, Logic Gate, neural network, Stochastic Computing (SC)

Category(s): Electrical > Computing Hardware, Electrical > Electronics & Semiconductors > Circuits

Synaptic Circuits Made From Transistors and Memory Capacitors (UCLA Case No. 2023-092)

Summary: UCLA researchers from the Department of Mechanical and Aerospace Engineering have developed a novel circuit architecture that emulates neural synapses for concurrent parallel computing. Background: Almost all modern computer chips consist of computing and learning processes that are implemented sequentially. To improve computing power,...

Published: 2/14/2025 | Inventor(s): Yong Chen, Zixuan Rong

Keywords(s): Application-Specific Integrated Circuit, Artifical Intelligence (Machine Learning, Data Mining), artificial electromagnetic materials, Artificial Intelligence, artificial intelligence augmentation, Artificial Neural Network, Artificial Neural Network Artificial Neuron, artificial-intelligent materials, autonomous control, computational efficiency, edge computing, Electronics & Semiconductors, Energy Efficiency, energy management, Integrated Circuit, Medical artificial intelligence (AI), Neuromorphic circuits, parallel processing, parallel signal processing, processor design, Semiconductor, semiconductor chip foundries, Semiconductor Device, Semiconductors, synaptic resistor (synstor)

Category(s): Electrical, Electrical > Electronics & Semiconductors, Electrical > Signal Processing, Materials, Materials > Semiconducting Materials, Software & Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning