Related papers: Evaluating Ternary Adders using a hybrid Memristor…
3T TRAM with doping profile (P+PNPNN+) is experimentally demonstrated on a silicon photonic platform. By using additional implant layers, this device provides excellent memory performance compared to the conventional structure (PNPN). TCAD…
Activation functions are widely used in neural networks to decide the activation value of the neural unit based upon linear combinations of the weighted inputs. The effective implementation of activation function is highly important, as…
Memristors are an emerging technology that enables artificial intelligence (AI) accelerators with high energy efficiency and radiation robustness -- properties that are vital for the deployment of AI on-board spacecraft. However, space…
With power consumption becoming a critical processor design issue, specialized architectures for low power processing are becoming popular. Several studies have shown that neural networks can be used for signal processing and pattern…
Multistate memory systems have the ability to store and process more data in the same physical space as binary memory systems, making them a potential alternative to existing binary memory systems. In the past, it has been demonstrated that…
Serial connection of multiple memory cells using perpendicular magnetic tunnel junctions (pMTJ) is proposed as a way to increase magnetic random access memory (MRAM) storage density. Multi-bit storage element is designed using pMTJs…
Resistance switching devices are of special importance because of their application in resistive memories (RRAM) which are promising candidates for replacing current nonvolatile memories and realize storage class memories. These devices…
The paper presents a systematic study and implementation of a reconfigurable combinatorial multi-operand adder for use in Deep Learning systems. The size of carry changes with the number of operands and hence a reliable algorithm to…
The advent of memristors and resistive switching has transformed solid state physics, enabling advanced applications such as neuromorphic computing. Inspired by these developments, we introduce the concept of Mem-emitters, devices that…
In this paper, we briefly review the concept of memory circuit elements, namely memristors, memcapacitors and meminductors, and then discuss some applications by focusing mainly on the first class. We present several examples, their…
Moore's law has long served the semiconductor industry as the driving force for producing ever-advancing electronics technologies. However, given the economic implications and technological challenges associated with the present…
Data-intensive computing tasks, such as training neural networks, are crucial for artificial intelligence applications but often come with high energy demands. One promising solution is to develop specialized hardware that directly maps…
Memristor technologies have been rapidly maturing for the past decade to support the needs of emerging memory, artificial synapses, logic gates and bio-signal processing applications. So far, however, most concepts are developed by…
Deep learning has made remarkable progress in various tasks, surpassing human performance in some cases. However, one drawback of neural networks is catastrophic forgetting, where a network trained on one task forgets the solution when…
Memristor crossbar architecture is one of the most popular circuit configurations due to its wide range of practical applications. The crossbar architecture can emulate the weighted summation operation, called multiply and accumulate…
Over the last decade, memristive devices have been widely adopted in computing for various conventional and unconventional applications. While the integration density, memory property, and nonlinear characteristics have many benefits,…
The advent of nanoscale memristors raised hopes of being able to build CMOL (CMOS/nanowire/moLecular) type ultra-dense in-memory-computing circuit architectures. In CMOL, nanoscale memristors would be fabricated at the intersection of…
Memristive crossbar arrays enable in-memory computing by performing parallel analog computations directly within memory, making them well-suited for machine learning, neural networks, and neuromorphic systems. However, despite their…
Memristors offer significant advantages as in-memory computing devices due to their non-volatility, low power consumption, and history-dependent conductivity. These attributes are particularly valuable in the realm of neuromorphic circuits…
Memristors have been suggested as a novel route to neuromorphic computing based on the similarity between them and neurons (specifically synapses and ion pumps). The d.c. action of the memristor is a current spike which imparts a short-term…