In-Memory Sensing and Computing for Cancer Diagnostics: A Perspective Paper

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The rapid evolution of Machine Learning (ML) workloads, particularly Deep Neural Networks (DNNs) and Transformer-based models, has intensified demands on computing architectures, highlighting the limitations of traditional von Neumann systems due to the me ...

The evolution of electronics has largely relied on downscaling to meet the continuous needs for faster and highly integrated devices(1). As the channel length is reduced, however, classic electronic devices face fundamental issues that hinder exploiting ma ...

Large deep neural network (DNN) models pose the key challenge to energy efficiency due to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or SRAM operations. It motivates the intensive research on model compression wit ...

A random access memory array including a plurality of local memory group ways, each local memory group way including, a plurality of local memory groups, each local memory group including, a memory column including a plurality of memory cells, a pair of lo ...

Originally discovered in condensed matter systems, topological insulators (TIs) have been ubiquitously extended to various fields of classical wave physics including photonics, phononics, acoustics, mechanics, and microwaves. In the bulk, like any other in ...

Electronic micro and nano-devices are suitable tools to monitor the activity of many individual neurons over mesoscale networks. However the inorganic materials currently used in microelectronics are barely accepted by neural cells and tissues, thus limiti ...

This dissertation proposes a novel accumulative test method for MEMS-based, commercial off-the-shelves inertial measure units (IMUs) from the automotive field. The need for clarification and augmentation of the corpus of knowledge on MEMS reliability for s ...

Resonant-type power converters are supposed to generate zero switching losses during soft-switching operation. In recent research, unexpected switching losses were reported in these converters, which were attributed to a large-signal hysteresis observed in ...

In-Memory Sensing and Computing for Cancer Diagnostics: A Perspective Paper

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Stop Wasting your Cache! Bringing Machine Learning into Cache Computing

Electronic metadevices for terahertz applications

Non-Structured DNN Weight Pruning--Is It Beneficial in Any Platform?

Associativity-agnostic in-cache computing memory architecture optimized for multiplication

Analog signal processing with topological metamaterials

Introducing a biomimetic coating for graphene neuroelectronics: toward in-vivo applications

New Reliability Assessment of MEMS Components under Accumulative Testing for Space Applications

Output-Capacitance Hysteresis Losses of Field-Effect Transistors