Circuits Summaries

Energy efficient, scalable neuromorphic computing with Growth Transform Neural Network
Chakrabartty, Shantanu ; Gangopadhyay, Ahana
T-019112

— Technology Description Engineers in Prof. Shantanu Chakrabartty’s laboratory have developed Growth Transform Neural Network (GTNN), a flexible system for designing scalable neuromorphic processors for use in deep learning systems and support vector machines. GTNN frames the neuromorphic syst…

Learnable, scalable, energy efficient analog-to-digital interfaces and their automated design
Chakrabarti, Ayan ; Zhang, Xuan "Silvia"
T-017967

— Technology Description Engineers in Prof. Xuan “Silvia” Zhang’s laboratory have developed a unifying design and optimization paradigm to automate the design of analog-to-digital interfaces and create scalable, general purpose analog and mixed signal (AMS) blocks that employ machi…

High-performance, nanoengineered two-phase cooling system for high powered electronics
Agonafer, Damena ; Ma, Binjian ; Shan, Li
T-018032

— Technology Description Engineers in Prof. Damena Agonafer’s laboratory have developed a highly-efficient, compact, modular evaporative cooling platform with materials and nanostructured geometries designed to greatly enhance thermal management of 3D integrated circuits, power converters and …

Controlling charge doping in 2D materials
Balgley, Jesse ; Henriksen, Erik
T-019602

— Technology Description Researchers at Washington University in St. Louis have developed a method to control charge doping in 2D materials like graphene. This method uses α-RuCl3 to create pn junctions at a smaller scale than silicon transistors. While α-RuCl3 efficiently removes el…