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Technical Paper Round-Up: July 26


New technical papers added to Semiconductor Engineering’s library this week. [table id=41 /] Semiconductor Engineering is in the process of building this library of research papers. Please send suggestions (via comments section below) for what else you’d like us to incorporate. If you have research papers you are trying to promote, we will review them to see if they are a good fit f... » read more

HW/SW Co-Design to Configure DNN Models On Energy Harvesting Devices


New technical paper titled "EVE: Environmental Adaptive Neural Network Models for Low-Power Energy Harvesting System" was published by researchers at UT San Antonio, University of Connecticut, and Lehigh University. According to the abstract: "This paper proposes EVE, an automated machine learning (autoML) co-exploration framework to search for desired multi-models with shared weights for... » read more

Technical Paper Round-Up: March 15


Research is expanding across a variety of semiconductor-related topics, from security to flexible substrates and chiplets. Unlike in the past, when work was confined to some of the largest universities, that research work is now being spread across a much broader spectrum of schools on a global basic, including joint research involving schools whose names rarely appeared together. Among the ... » read more

Mapping Transformation Enabled High-Performance and Low-Energy Memristor-Based DNNs


Abstract: "When deep neural network (DNN) is extensively utilized for edge AI (Artificial Intelligence), for example, the Internet of things (IoT) and autonomous vehicles, it makes CMOS (Complementary Metal Oxide Semiconductor)-based conventional computers suffer from overly large computing loads. Memristor-based devices are emerging as an option to conduct computing in memory for DNNs to make... » read more

FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator


Abstract: "Recent work demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vector multiplication—the intensive and key computation in deep neural networks (DNNs). One key problem is the weights that are signed values. However, in a ReRAM crossbar, weights are stored as conductance of... » read more

System Bits: Jan. 23


Artificial synapse for “brain-on-a-chip” portable AI devices In the emerging field of neuromorphic computing, researchers are attempting to design computer chips that work like the human brain, which, instead of carrying out computations based on binary, on/off signaling like digital chips do today, the elements of a brain-on-a-chip would work in an analog fashion, exchanging a gradient of... » read more

Power/Performance Bits: May 23


Biosupercapacitor Researchers from UCLA and the University of Connecticut designed a biological supercapacitor, a new biofriendly energy storage system which operates using ions from fluids in the human body. The device is harmless to the body's biological systems, say the researchers, and could lead to longer-lasting cardiac pacemakers and other implantable medical devices. The supercapa... » read more

Power/Performance Bits: Oct. 4


Solar battery Chemists at the University of Wisconsin–Madison and the King Abdullah University of Science and Technology in Saudi Arabia integrated solar cells with a large-capacity battery in a single device that eliminates the usual intermediate step of making electricity and, instead, transfers the energy directly to the battery's electrolyte. The team used a redox flow battery, or R... » read more