Modeling Optical Loss And Crosstalk Noise For Silicon-Photonic-Based Neural Networks Of Different Scales 


A technical paper titled “Analysis of Optical Loss and Crosstalk Noise in MZI-based Coherent Photonic Neural Networks" was published by researchers at Colorado State University (Fort Collins), NVIDIA, and Arizona State University. Abstract: "With the continuous increase in the size and complexity of machine learning models, the need for specialized hardware to efficiently run such model... » read more

A Search Framework That Optimizes Hybrid-Device IMC Architectures For DNNs, Using Chiplets


A technical paper titled “HyDe: A Hybrid PCM/FeFET/SRAM Device-search for Optimizing Area and Energy-efficiencies in Analog IMC Platforms” was published by researchers at Yale University. Abstract: "Today, there are a plethora of In-Memory Computing (IMC) devices- SRAMs, PCMs & FeFETs, that emulate convolutions on crossbar-arrays with high throughput. Each IMC device offers its own pr... » read more

Analog Circuits Enabling Learning in Mixed-Signal Neuromorphic SNNs, With Tristate Stability and Weight Discretization Circuits


A technical paper titled “Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks” was published by researchers at University of Zurich and ETH Zurich. Abstract: "Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their spiking neural network circui... » read more

CNN Hardware Architecture With Weights Generator Module That Alleviates Impact Of The Memory Wall


A technical paper titled “Mitigating Memory Wall Effects in CNN Engines with On-the-Fly Weights Generation” was published by researchers at Samsung AI Center and University of Cambridge. Abstract: "The unprecedented accuracy of convolutional neural networks (CNNs) across a broad range of AI tasks has led to their widespread deployment in mobile and embedded settings. In a pursuit for high... » read more

A Photonic Circuit Architecture Allowing Faster, More Efficient Transfer of Large Amounts of Data


A technical paper titled "Massively scalable Kerr comb-driven silicon photonic link" was published by researchers at Columbia University and Air Force Research Laboratory. Abstract: "The growth of computing needs for artificial intelligence and machine learning is critically challenging data communications in today’s data-centre systems. Data movement, dominated by energy costs and limi... » read more

Improving Performance Of Artificial Intelligence And Quantum Computers


A technical paper titled “Gate-tunable superconducting diode effect in a three-terminal Josephson device” was published by researchers at University of Minnesota, University of California Santa Barbara, and Stanford University. Abstract: "The phenomenon of non-reciprocal critical current in a Josephson device, termed the Josephson diode effect, has garnered much recent interest. Realizati... » read more

SB MOSFET-Based Ultra-Low Power Real-Time Neurons for Neuromorphic Computing (Indian Institute of Technology)


A technical paper titled “Schottky Barrier MOSFET Enabled Ultra-Low Power Real-Time Neuron for Neuromorphic Computing” was published by researchers at the Indian Institute of Technology (IIT) Bombay. Abstract: "Energy-efficient real-time synapses and neurons are essential to enable large-scale neuromorphic computing. In this paper, we propose and demonstrate the Schottky-Barrier MOSFE... » 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

Object Detection CNN Suitable For Edge Processors With Limited Memory


A technical paper titled “TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers” was published by researchers at ETH Zurich. Abstract: "This paper introduces a highly flexible, quantized, memory-efficient, and ultra-lightweight object detection network, called TinyissimoYOLO. It aims to enable object detection on microcontrol... » read more

A PIM Architecture That Supports Floating Point-Precision Computations Within The Memory Chip


A technical paper titled “FlutPIM: A Look-up Table-based Processing in Memory Architecture with Floating-point Computation Support for Deep Learning Applications” was published by researchers at Rochester Institute of Technology and George Mason University. Abstract: "Processing-in-Memory (PIM) has shown great potential for a wide range of data-driven applications, especially Deep Learnin... » read more

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