相关论文: Large-Scale Integrated Vector-Matrix Multiplicatio…
Two-dimensional semiconductors such as MoS2 are an emerging material family with wide-ranging potential applications in electronics, optoelectronics and energy harvesting. Large-area growth methods are needed to open the way to the…
Collocated data processing and storage are the norm in biological systems. Indeed, the von Neumann computing architecture, that physically and temporally separates processing and memory, was born more of pragmatism based on available…
2D nanoelectronics based on single-layer MoS2 offers great advantages for both conventional and ubiquitous applications. This paper discusses the large-scale CVD growth of single-layer MoS2 and fabrication of devices and circuits for the…
This paper presents a programmable in-memory-computing processor, demonstrated in a 65nm CMOS technology. For data-centric workloads, such as deep neural networks, data movement often dominates when implemented with today's computing…
Utilizing the excess energy of photoexcitation that is otherwise lost as thermal effects can improve the efficiency of next-generation light-harvesting devices. Multiple exciton generation (MEG) in semiconducting materials yields two or…
Two-dimensional (2D) materials present an exciting opportunity for devices and systems beyond the von Neumann computing architecture paradigm due to their diversity of electronic structure, physical properties, and atomically-thin, van der…
Two-dimensional (2D) semiconductors have been suggested both for ultimately-scaled field-effect transistors (FETs) and More-than-Moore nanoelectronics. However, these targets can not be reached without accompanying gate insulators which are…
Large-scale floating-point matrix multiplication is a fundamental kernel in many scientific and engineering applications. Most existing work only focus on accelerating matrix multiplication on FPGA by adopting a linear systolic array. This…
We report a sequential two-step vapor deposition process for growing mixed-dimensional van der Waals (vdW) materials, specifically Te nanowires (1D) and MoS$_2$ (2D), on a single SiO$_2$ wafer. Our growth technique offers a unique potential…
We propose an extremely energy-efficient mixed-signal approach for performing vector-by-matrix multiplication in a time domain. In such implementation, multi-bit values of the input and output vector elements are represented with…
The new generation of machine learning processors have evolved from multi-core and parallel architectures that were designed to efficiently implement matrix-vector-multiplications (MVMs). This is because at the fundamental level, neural…
Memristive devices have drawn considerable research attention due to their potential applications in non-volatile memory and neuromorphic computing. The combination of resistive switching devices with light-responsive materials is…
We report for the first time the direct growth of Molybdenum disulfide (MoS$_2$) monolayers on nanostructured silicon-on-insulator waveguides. Our results indicate the possibility of utilizing the Chemical Vapour Deposition (CVD) on…
Large capacitance enhancement is useful for increasing the gate capacitance of field-effect transistors (FETs) to produce low-energy-consuming devices with improved gate controllability. We report strong capacitance enhancement effects in a…
Matrix multiplication is a fundamental operation in both training of neural networks and inference. To accelerate matrix multiplication, Graphical Processing Units (GPUs) provide it implemented in hardware. Due to the increased throughput…
As one of the most important members of the two dimensional chalcogenide family, molybdenum disulphide (MoS2) has played a fundamental role in the advancement of low dimensional electronic, optoelectronic and piezoelectric designs. Here, we…
Brain-inspired non-Boolean computing offers intrinsic error tolerance and parallelism, but its practical deployment is limited by the lack of compact, energy-efficient spiking hardware compatible with large-scale integration. Mott…
Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapting to new data. Memory-augmented neural networks enhance…
In this work, we propose a ferroelectric FET(FeFET) time-domain compute-in-memory (TD-CiM) array as a homogeneous processing fabric for binary multiplication-accumulation (MAC) and content addressable memory (CAM). We demonstrate that: i)…
Defects in atomically thin materials can drive new functionalities and expand applications to multifunctional systems that are monolithically integrated. An ability to control formation of defects during the synthesis process is an…