Related papers: Temperature-Resilient Analog Neuromorphic Chip in …
In this work, we present a hybrid memory bit cell - collocated SRAM and DRAM (CRAM) consisting of 11 transistors for in-memory computing (IMC) based image restoration (IR) and region proposal (RP). A robust RP updated algorithm is proposed…
The quest for energy-efficient, scalable neuromorphic computing has elevated compute-in-memory (CIM) architectures to the forefront of hardware innovation. While memristive memories have been extensively explored for synaptic implementation…
For neuromorphic engineering to emulate the human brain, improving memory density with low power consumption is an indispensable but challenging goal. In this regard, emerging RRAMs have attracted considerable interest for their unique…
Silicon photonics is a leading platform for realizing the vast numbers of physical qubits needed for useful quantum information processing because it leverages mature complementary metal-oxide-semiconductor (CMOS) manufacturing to integrate…
Binary neural networks (BNNs) that use 1-bit weights and activations have garnered interest as extreme quantization provides low power dissipation. By implementing BNNs as computing-in-memory (CIM), which computes multiplication and…
Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by enabling low-latency, high-speed, and energy-efficient computations. However, conventional photonic tensor cores face significant…
Analog in-memory computing (AIMC) accelerators enable efficient deep neural network computation directly within memory using resistive crossbar arrays, where model parameters are represented by the conductance states of memristive devices.…
Recent works propose neural network- (NN-) inspired analog-to-digital converters (NNADCs) and demonstrate their great potentials in many emerging applications. These NNADCs often rely on resistive random-access memory (RRAM) devices to…
Traditional von Neumann architecture based processors become inefficient in terms of energy and throughput as they involve separate processing and memory units, also known as~\textit{memory wall}. The memory wall problem is further…
All quantum optomechanics experiments to date operate at cryogenic temperatures, imposing severe technical challenges and fundamental constraints. Here we present a novel design of on-chip mechanical resonators which exhibit fundamental…
Neural systems use the same underlying computational substrate to carry out analog filtering and signal processing operations, as well as discrete symbol manipulation and digital computation. Inspired by the computational principles of…
As a promising alternative to the Von Neumann architecture, in-memory computing holds the promise of delivering high computing capacity while consuming low power. Content addressable memory (CAM) can implement pattern matching and distance…
Compute-in-memory (CIM) presents an attractive approach for energy-efficient computing in data-intensive applications. However, the development of suitable memory designs to achieve high-performance CIM remains a challenging task. Here, we…
Neuromorphic engineering is essentially the development of artificial systems, such as electronic analog circuits that employ information representations found in biological nervous systems. Despite being faster and more accurate than the…
Analog compute-in-memory (CIM) in static random-access memory (SRAM) is promising for accelerating deep learning inference by circumventing the memory wall and exploiting ultra-efficient analog low-precision arithmetic. Latest analog CIM…
Presented are two techniques that are designed to help streamline the discretization of complex vascular geometries within the numerical modeling process. The first method integrates multiple software tools into a single pipeline which can…
Memristive circuit elements constitute a cornerstone for novel electronic applications, such as neuromorphic computing, called to revolutionize information technologies. By definition, memristors are sensitive to the history of electrical…
Neuromorphic computing, inspired by biological neural systems, has emerged as a promising approach for ultra-energy-efficient data processing by leveraging analog neuron structures and spike-based computation. However, its application in…
The rapid scaling of artificial neural networks has exposed fundamental limitations of conventional von Neumann computing architectures. In these systems, the physical separation between memory and processing creates a bottleneck, as…
Hybrid integration of plasmonics and Si photonics is a promising architecture for global microprocessor interconnects. To this end, practical plasmonic devices not only should provide athermal, broadband operation over wavelength-scale…