Related papers: Design of an Analog Memory Cell in 0.25 micron CMO…
The smart integrated systems of tomorrow would demand a combination of micromechanical components and traditional electronics. On-chip solutions will be the ultimate goal. One way of making such systems is to implement the mechanical parts…
Quantum memories are key components in quantum information networks. Their ability to store and retrieve information on demand makes repeat-until-success strategies scalable. Warm alkali-metal vapours are interesting candidates for the…
Analog processing-using-memory (PUM; a.k.a. in-memory computing) makes use of electrical interactions inside memory arrays to perform bulk matrix-vector multiplication (MVM) operations. However, many popular matrix-based kernels need to…
Emerging nanodevices such as resistive memories are being considered for hardware realizations of a variety of artificial neural networks (ANNs), including highly promising online variants of the learning approaches known as reservoir…
Phase change memory (PCM) is an emerging high speed, high density, high endurance, and scalable non-volatile memory technology which utilizes the large resistivity contrast between the amorphous and crystalline phases of chalcogenide…
Quantum memories are essential for photonic quantum technologies, enabling long-distance quantum communication and serving as delay units in quantum computing. Hot atomic vapors using electromagnetically induced transparency provide a…
The increasing density of transistors in Integrated Circuits (ICs) has enabled the development of highly integrated Systems-on-Chip (SoCs) and, more recently, Multiprocessor Systems-on-Chip (MPSoCs). To address scalability challenges in…
Memory cells are an important building block of digital electronics. We combine here the unique electronic properties of semiconducting monolayer MoS2 with the high conductivity of graphene to build a 2D heterostructure capable of…
The demand for high-density data storage with ultrafast accessibility motivates the search for new memory implementations. Ideally such storage devices should be robust to input error and to unreliability of individual elements; furthermore…
Kernel functions are vital ingredients of several machine learning algorithms, but often incur significant memory and computational costs. We introduce an approach to kernel approximation in machine learning algorithms suitable for…
For decades, conventional computers based on the von Neumann architecture have performed computation by repeatedly transferring data between their processing and their memory units, which are physically separated. As computation becomes…
Analog in-memory computing is an emerging paradigm designed to efficiently accelerate deep neural network workloads. Recent advancements have focused on either inference or training acceleration. However, a unified analog in-memory…
Quantum memory is the core device for the construction of large-scale quantum networks. For scalable and convenient practical applications, integrated optical memories, especially on-chip optical memories, are crucial requirements because…
The significant advancements in integrated photonics have enabled high-speed and energy efficient systems for various applications from data communications and high-performance computing, to medical diagnosis, sensing and ranging. However,…
AI chips commonly employ SRAM memory as buffers for their reliability and speed, which contribute to high performance. However, SRAM is expensive and demands significant area and energy consumption. Previous studies have explored replacing…
We report a resistance based threshold logic family useful for mimicking brain like large variable logic functions in VLSI. A universal Boolean logic cell based on an analog resistive divider and threshold logic circuit is presented. The…
The need to repeatedly shuttle around synaptic weight values from memory to processing units has been a key source of energy inefficiency associated with hardware implementation of artificial neural networks. Analog in-memory computing…
The memristance of a memristor depends on the amount of charge flowing through it and when current stops flowing through it, it remembers the state. Thus, memristors are extremely suited for implementation of memory units. Memristors find…
The purpose of this project was to design and implement a pipeline Analog-to-Digital Converter using 0.35um CMOS technology. Initial requirements of a 25-MHz conversion rate and 8-bits of resolution where the only given ones. Although…
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,…