Molecular cellular networks: A non von Neumann architecture for molecular electronics

Lent, Craig S.; Henderson, Kenneth W.; Kandel, S. Alex; Corcelli, Steven A.; Snider, Gregory L.; Orlov, Alexei O.; Kogge, Peter M.; Niemier, Michael; Brown, Ryan; Christie, John A.; Wasio, Natalie A.; Quardokus, Rebecca C.; Forrest, Ryan P.; Peterson, Jacob P.; Silski, Angela M.; Turner, David A.; Blair, Enrique P.; Lu, Yuhui

doi:10.1109/icrc.2016.7738699

Cited by 19 publications

(4 citation statements)

References 47 publications

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“…As a result, the alternatives to von Neumann systems started to emerge. [6,7] One turns to alternative hardware architectures and purposebuilt devices to keep up with the scaling performance. As such, universal quantum computing promises to decrease the algorithmic complexity of solving challenging tasks by exploiting the entangled states.…”

Section: Doi: 101002/qute202300055mentioning

confidence: 99%

Analog Photonics Computing for Information Processing, Inference, and Optimization

Nikita

Berloff

2023

Adv Quantum Tech

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This review presents an overview of the current state‐of‐the‐art in photonics computing, which leverages photons, photons coupled with matter, and optics‐related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special‐purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.

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Section: Doi: 101002/qute202300055mentioning

confidence: 99%

Analog Photonics Computing for Information Processing, Inference, and Optimization

Nikita

Berloff

2023

Adv Quantum Tech

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“…Additionally, the circulating active domains have access to four separate straight tracks. This scheme, referred to as a molecular cellular network [36], supports the routing of data and the flow of calculations in all directions throughout the network, as well as feedback and memory. Memory and logic may be distributed throughout the entire grid, highlighting the non-von-Neumann nature of this computational paradigm.…”

Section: The Molecular Cellular Networkmentioning

confidence: 99%

Clock Topologies for Molecular Quantum-Dot Cellular Automata

Blair

Lent

2018

JLPEA

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Quantum-dot cellular automata (QCA) is a low-power, non-von-Neumann, general-purpose paradigm for classical computing using transistor-free logic. Here, classical bits are encoded on the charge configuration of individual computing primitives known as “cells.” A cell is a system of quantum dots with a few mobile charges. Device switching occurs through quantum mechanical inter-dot charge tunneling, and devices are interconnected via the electrostatic field. QCA devices are implemented using arrays of QCA cells. A molecular implementation of QCA may support THz-scale clocking or better at room temperature. Molecular QCA may be clocked using an applied electric field, known as a clocking field. A time-varying clocking field may be established using an array of conductors. The clocking field determines the flow of data and calculations. Various arrangements of clocking conductors are laid out, and the resulting electric field is simulated. It is shown that that control of molecular QCA can enable feedback loops, memories, planar circuit crossings, and versatile circuit grids that support feedback and memory, as well as data flow in any of the ordinal grid directions. Logic, interconnect and memory now become indistinguishable, and the von Neumann bottleneck is avoided.

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“…Compute-in-memory (CIM) is a fast-rising paradigm of neural network hardware accelerators for their potential of overcoming the "memory wall" bottleneck [1]- [4]. Among them, resistive random-access memory (RRAM) based CIM is deemed a promising direction thanks to several advantages [5].…”

Section: Introductionmentioning

confidence: 99%

A High-Parallelism RRAM-Based Compute-In-Memory Macro With Intrinsic Impedance Boosting and In-ADC Computing

Xie

2023

IEEE J. Explor. Solid-State Comput. Devices Circuits

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Resistive random access memory (RRAM) is considered to be a promising compute-in-memory (CIM) platform; however, they tend to lose energy efficiency quickly in highthroughput and high-resolution cases. This paper proposes an analog-oriented RRAM array/read circuits co-design technique to solve the trade-offs between distortion, power consumption, throughput, and chip area. In previous works, the RRAM array is built in a digital mindset that access transistors only serve as switches. Instead, this work investigates a new perspective that utilizes access transistors as common-gate current buffers which reduce the operating current and amplify the output impedance. To further decrease the complexity of peripheral circuits, the idea of In-ADC Computing (IAC) is proposed, which performs shift-add operation together with analog-to-digital conversion. Benefiting from proposed ideas, a pre-trained VGG-8 network based on the CIFAR-10 dataset can be implemented and an accuracy of 87.2% is achieved with 8.9 TOPS/W energy efficiency (for 8-bit MAC operation), demonstrating that the proposed techniques enable low-distortion partial sum results while still be able to operate in a power-efficient way.

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Molecular cellular networks: A non von Neumann architecture for molecular electronics

Cited by 19 publications

References 47 publications

Analog Photonics Computing for Information Processing, Inference, and Optimization

Analog Photonics Computing for Information Processing, Inference, and Optimization

Clock Topologies for Molecular Quantum-Dot Cellular Automata

A High-Parallelism RRAM-Based Compute-In-Memory Macro With Intrinsic Impedance Boosting and In-ADC Computing

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