21 papers · ranked by Valyu relevance
Ulmann, Bernd
—The ACHILLES heel of classic analog computers was the complex, error prone, and time consuming process of programming. This typically involved manually patching hundreds or even thousands of connections between individual computing elements as well as setting many precision 10-turn potentiometers manually, often…
Zhong Sun, Piergiulio Mannocci, Manuel Le Gallo, Abu Sebastian
In recent years, driven by the computational demands of data-intensive applications such as artificial intelligence and scientific computing, analog computing has gained renewed interest. Given the diversity of computational tasks and recent advancements in analog CMOS circuits and resistive memory technologies, we…
Bernd Ulmann
—Many people think of analog computing as a historic dead-end in computing. In fact, nothing could be further from the truth as analog computing – together with quantum computing – has the potential to bring computing to new levels with respect to raw computational power and energy efficiency. The following paper…
Xiangpeng Liang, Yanan Zhong, Jianshi Tang, Zhengwu Liu + 7 more
'Keyang Sun' 'Qingtian Zhang' 'Bin Gao' 'Hadi Heidari' 'He Qian' 'Huaqiang Wu'] Hardware implementation in resource-efficient reservoir computing is of great interest for neuromorphic engineering. Recently, various devices have been explored to implement hardware-based reservoirs. However, most studies were mainly…
Matteo Nerini, Bruno Clerckx
—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 analog computers (MiLACs), and their applications in signal processing and…
Robert Frederik Uy, Viet Phuong Bui
Wave-based analog computing has recently emerged as a promising computing paradigm due to its potential for high computational efficiency and minimal crosstalk. Although low-frequency acoustic analog computing systems exist, their bulky size makes it difficult to integrate them into chips that are compatible with…
Tao Zhu, Bowen Zhu, Shicheng Zhang, Keren Li + 12 more
In the “post-Moore era,” the growing challenges in traditional digital computing have driven renewed interest in analog computing. Photonic analog computing has emerged as an effective paradigm for overcoming the fundamental bottlenecks that constrain conventional analog accelerators, especially suited for high-speed…
James T. Meech, Vasileios Tsoutsouras, Phillip Stanley‐Marbell
Most modern computing tasks have digital electronic input and output data. Due to these constraints imposed by real-world use cases of computer systems, any analog computing accelerator, whether analog electronic or optical, must perform an analog-to-digital conversion on its input data and a subsequent…
Robert Frederik Uy, Viet Phuong Bui
Wave-based analog computing is a new computing paradigm heralded as a potentially superior alternative to existing digital computers. Currently, there are optical and low-frequency acoustic analog Fourier transformers. However, the former suffers from phase retrieval issues, and the latter is too physically bulky for…
Ruofan Li, Min Song, Zhe Guo, Shihao Li + 17 more
'Yufeng Tian' 'Zhenjiang Chen' 'Yi Bao' 'Jinsong Cui' 'Yan Xu' 'Yaoyuan Wang' 'Wei Tong' 'Zhe Yuan' 'Yan Cui' 'Li Xi' 'Dan Feng' 'Xiaofei Yang' 'Xuecheng Zou' 'Jeongmin Hong' 'Long You'] Title: Abstract Analog arithmetic operations are the most fundamental mathematical operations used in image and signal processing as…
Vasudev S. Mallan, Anitha Gopi, Chithra Reghuvaran, Aswani A. Radhakrishnan + 1 more
'Aswani A. Radhakrishnan' 'Alex James'] Intelligent sensor systems are essential for building modern Internet of Things applications. Embedding intelligence within or near sensors provides a strong case for analog neural computing. However, rapid prototyping of analog or mixed signal spiking neural computing is a…
Alessio Antolini, Francesco Zavalloni, Andrea Lico, Said Quqa + 6 more
'Lorenzo Greco' 'Mauro Mangia' 'Fabio Pareschi' 'Marco Pasotti' 'Eleonora Franchi Scarselli' 'Charith Perera'] Phase Change Memory (PCM) has emerged as a promising non-volatile memory technology with significant applications in both edge computing and analog in-memory computing. This paper synthesizes recent research…
Sudarsan Sadasivuni, Sumukh Prashant Bhanushali, Imon Banerjee, Arindam Sanyal
'Arindam Sanyal'] This work presents an on-chip analog-to-information conversion technique that utilizes analog hyper-dimensional computing based on reservoir-computing paradigm to process electrocardiograph (ECG) signals locally in-sensor and reduce radio frequency transmission by more than three orders-of-magnitude.…
Orkan Telhan, Jake Winiski, Damen Schaak, Michael Siegel + 2 more
We introduce a neuromorphic computing substrate based on PEDOT:PSS-infused mycelium, a biofabricated, morphologically tunable material that can be engineered into electrically active components including resistors, capacitors, and non-linear elements. Leveraging the principles of physical reservoir computing, we…
Maryada, Chiara De Luca, Arianna Rubino, Chenxi Wen + 4 more
Cortical microcircuits play a fundamental role in natural intelligence. While they inspired a wide range neural computation models and artificial intelligence algorithms, few attempts have been made to directly emulate them with an electronic computational substrate that uses the same physics of computation. Here we…
Maryada, Saray Soldado-Magraner, Martino Sorbaro, Rodrigo Laje + 2 more
Many neural computations emerge from self-sustained patterns of activity in recurrent neural circuits, which rely on balanced excitation and inhibition. Neuromorphic electronic circuits that use the physics of silicon to emulate neuronal dynamics represent a promising approach for implementing the brain’s computational…
Justin H. Letendre, Benjamin H. Weinberg, Marisa Mendes, Jeffery M. Marano + 6 more
Living cells perform sophisticated computations that guide them toward discrete states. Synthetic genetic circuits are powerful tools for programing these computations, where transcription-regulatory networks and DNA recombination are the two dominant paradigms for implementing these systems. While each strategy…
Frank Britto Bisso, Durga Shree, Yinan Zhu, Christian Cuba Samaniego
Cells have evolved to sense a wide range of input combinations and integrate those signals through signaling pathways to produce context-specific responses, such as differentiation, cell-type specification, and patterning. To replicate this information-processing capacity, synthetic biology has developed large-scale…
Gina Partipilo, Sarah M. Coleman, Yang Gao, Ismar E. Miniel Mahfoud + 3 more
A significant advancement in synthetic biology is the development of synthetic gene circuits with predictive Boolean logic. However, there is no universally accepted or applied statistical test to analyze the performance of these circuits. Many basic statistical tests fail to capture the predicted logic (OR, AND, etc.)…
Aliye Hazal Koyuncu, Jacopo Movilli, Sevil Sahin, Dmitrii V. Kriukov + 2 more
This work describes a competing activation network, which is regulated by chemical feedback at the liquid-surface interface. Feedback loops dynamically tune the concentration of chemical components in living systems, thereby controlling regulatory processes in neural, genetic, and metabolic networks. Advances in…
Yusuke Shinji, Hirotsugu Okuno, Yutaka Hirata
The cerebellum plays a central role in motor control and learning. Its neuronal network architecture, firing characteristics of component neurons, and learning rules at their synapses have been well understood in terms of anatomy and physiology. A realistic artificial cerebellum with mimetic network architecture and…