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SOT-MRAM-based CIM architecture for a CNN model


New research paper "In-Memory Computing Architecture for a Convolutional Neural Network Based on Spin Orbit Torque MRAM", from National Taiwan University, Feng Chia University, Chung Yuan Christian University. Abstract "Recently, numerous studies have investigated computing in-memory (CIM) architectures for neural networks to overcome memory bottlenecks. Because of its low delay, high energ... » read more

A graph placement methodology for fast chip design


Abstract "Chip floorplanning is the engineering task of designing the physical layout of a computer chip. Despite five decades of research1, chip floorplanning has defied automation, requiring months of intense effort by physical design engineers to produce manufacturable layouts. Here we present a deep reinforcement learning approach to chip floorplanning. In under six hours, our method autom... » read more

Fabs Drive Deeper Into Machine Learning


Advanced machine learning is beginning to make inroads into yield enhancement methodology as fabs and equipment makers seek to identify defectivity patterns in wafer images with greater accuracy and speed. Each month a wafer fabrication factory produces tens of millions of wafer-level images from inspection, metrology, and test. Engineers must analyze that data to improve yield and to reject... » read more