Related papers: Process, Bias and Temperature Scalable CMOS Analog…
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…
Over-the-air computation (OAC) leverages the physical superposition property of wireless multiple access channels (MACs) to compute functions while communication occurs, enabling scalable and low-latency processing in distributed networks.…
In the pursuit of quantum computing, solid-state quantum systems, particularly superconducting ones, have made remarkable advancements over the past two decades. However, achieving fault-tolerant quantum computing for next-generation…
Temperature imaging of nanoscale systems is a fundamental problem which has myriad potential technological applications. For example, nanoscopic cold spots can be used for spot cooling electronic components while hot spots could be used for…
With the advent of high-speed, high-precision, and low-power mixed-signal systems, there is an ever-growing demand for accurate, fast, and energy-efficient analog-to-digital (ADCs) and digital-to-analog converters (DACs). Unfortunately,…
Quantum computers have great potential to solve problems which are intractable on classical computers. However, quantum processors have not yet reached the required scale to run applications which outperform traditional computers. Leading…
The progress in neuromorphic computing is fueled by the development of novel nonvolatile memories capable of storing analog information and implementing neural computation efficiently. However, like most other analog circuits, these devices…
Deep learning hardware designs have been bottlenecked by conventional memories such as SRAM due to density, leakage and parallel computing challenges. Resistive devices can address the density and volatility issues, but have been limited by…
This paper explores the potential of cryogenic semiconductor computing and superconductor electronics as promising alternatives to traditional semiconductor devices. As semiconductor devices face challenges such as increased leakage…
Recently we have shown that an architecture based on resistive processing unit (RPU) devices has potential to achieve significant acceleration in deep neural network (DNN) training compared to today's software-based DNN implementations…
In this paper, we propose a novel semantic digital analog converter (sDAC) for the compatibility of semantic communications and digital communications. Most of the current semantic communication systems are based on the analog modulations,…
Approximate computing (AC) leverages the inherent error resilience and is used in many big-data applications from various domains such as multimedia, computer vision, signal processing, and machine learning to improve systems performance…
Analog In-memory Computing (AIMC) has emerged as a highly efficient paradigm for accelerating Deep Neural Networks (DNNs), offering significant energy and latency benefits over conventional digital hardware. However, state-of-the-art neural…
Analog electrical networks have long been investigated as energy-efficient computing platforms for machine learning, leveraging analog physics during inference. More recently, resistor networks have sparked particular interest due to their…
Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the market every day. Some of these have very severe Size, Weight and Power constraints whereas other devices have to handle very high…
System-level design, once the province of board designers, has now become a central concern for chip designers. Because chip design is a less forgiving design medium -- design cycles are longer and mistakes are harder to correct --…
In this paper, we realize the concept of analog computing using an array of engineered gradient dielectric meta-reflect-array. The proposed configuration consists of individual subwavelength silicon nanobricks in combination with fused…
We describe and analyze magnonic logic circuits enabling parallel data processing on multiple frequencies. The circuits combine bi-stable (digital) input/output elements and an analog core. The data transmission and processing within the…
Analog computing has been recently revived due to its potential for energy-efficient and highly parallel computations. In this two-part paper, we explore analog computers that linearly process microwave signals, named microwave linear…
Memory-centric computing aims to enable computation capability in and near all places where data is generated and stored. As such, it can greatly reduce the large negative performance and energy impact of data access and data movement, by…