Related papers: Process, Bias and Temperature Scalable CMOS Analog…
Bias-scalable analog computing is attractive for implementing machine learning (ML) processors with distinct power-performance specifications. For instance, ML implementations for server workloads are focused on higher computational…
Operations typically used in machine learning al-gorithms (e.g. adds and soft max) can be implemented bycompact analog circuits. Analog Application-Specific Integrated Circuit (ASIC) designs that implement these algorithms using techniques…
In analog neuromorphic chips, designers can embed computing primitives in the intrinsic physical properties of devices and circuits, heavily reducing device count and energy consumption, and enabling high parallelism, because all devices…
CMOS-transistors circuits have been used as a conventional approach for designing an analog multiplier in modern era of industrial electronics. However, previous studies have shown, that based on the working region of transistors, such as…
This paper presents an implementation of multilayer feed forward neural networks (NN) to optimize CMOS analog circuits. For modeling and design recently neural network computational modules have got acceptance as an unorthodox and useful…
Analog/mixed-signal circuit design is one of the most complex and time-consuming stages in the whole chip design process. Due to various process, voltage, and temperature (PVT) variations from chip manufacturing, analog circuits inevitably…
In this work, a new method for designing an analog circuit for deep sub-micron CMOS fabrication processes is proposed. The proposed method leverages the regression algorithms with the transistor circuit model to size a transistor in 0.18 um…
Matrix computation is ubiquitous in modern scientific and engineering fields. Due to the high computational complexity in conventional digital computers, matrix computation represents a heavy workload in many data-intensive applications,…
The past few decades have seen exponential growth in capabilities of digital electronics primarily due to the ability to scale Integrated Circuits (ICs) to smaller dimensions while attaining power and performance benefits. That scalability…
Analog and radio-frequency circuit design requires extensive exploration of both circuit topology and parameters to meet specific design criteria like power consumption and bandwidth. Designers must review state-of-the-art topology…
On-chip learning in a crossbar array based analog hardware Neural Network (NN) has been shown to have major advantages in terms of speed and energy compared to training NN on a traditional computer. However analog hardware NN proposals and…
The recent development in analog computation is reviewed in this paper. Analog computation was used in many applications where power and energy efficiency is of paramount importance. It is shown that by using innovative architecture and…
In this paper, we propose an analog pixel differencing circuit for differentiating pixels between frames directly from CMOS pixels. The analog information processing at sensor is a topic of growing appeal to develop edge AI devices. The…
Investigating the temporal behavior of digital circuits is a crucial step in system design, usually done via analog or digital simulation. Analog simulators like SPICE iteratively solve the differential equations characterizing the circuits…
Mixed-signal neuromorphic systems represent a promising solution for solving extreme-edge computing tasks without relying on external computing resources. Their spiking neural network circuits are optimized for processing sensory data…
We have developed a quantum annealing processor, based on an array of tunably coupled rf-SQUID flux qubits, fabricated in a superconducting integrated circuit process [1]. Implementing this type of processor at a scale of 512 qubits and…
Previous efforts on reconfigurable analog circuits mostly focused on specialized analog circuits, produced through careful co-design, or on highly reconfigurable, but relatively resource inefficient, accelerators that implement analog…
Analog mixed-signal (AMS) circuit architecture has evolved towards more digital friendly due to technology scaling and demand for higher flexibility/reconfigurability. Meanwhile, the design complexity and cost of AMS circuits has…
Neuromorphic engineering makes use of mixed-signal analog and digital circuits to directly emulate the computational principles of biological brains. Such electronic systems offer a high degree of adaptability, robustness, and energy…
Stochastic computing (SC) is an emerging computing technique that promises high density, low power, and error tolerant solutions. In SC, values are encoded as unary bitstreams and SC arithmetic circuits operate on one or more bitstreams. In…