相关论文: Scaling Analysis of Nanowire Phase Change Memory

In this paper, we combine communication-theoretic laws with known, practically verified results from circuit theory. As a result, we obtain closed-form theoretical expressions linking fundamental system design and environment parameters…

Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler for reliable AI systems. However, conventional hardware…

Scaling neural network models has delivered dramatic quality gains across ML problems. However, this scaling has increased the reliance on efficient distributed training techniques. Accordingly, as with other distributed computing…

Maintaining benefits of CMOS technology scaling is becoming challenging due to increased manufacturing complexities and unwanted passive power dissipations. This is particularly challenging in SRAM, where manufacturing precision and leakage…

Computing-in-memory (CIM) has attracted significant attentions in recent years due to its massive parallelism and low power consumption. However, current CIM designs suffer from large area overhead of small CIM macros and bad programmablity…

We study the performance of our previously proposed Projective Quantum Eigensolver (PQE) on IBM's quantum hardware in conjunction with error mitigation techniques. For a single qubit model of H$_2$, we find that we are able to obtain…

This paper studies the design and dynamic modelling of a novel thermal energy storage (TES) system combined with a refrigeration system based on phase change materials (PCM). Cold-energy production supported by TES systems is a very…

Thermoelectric effects are envisioned to reduce programming currents in nanopillar phase change memory cells. However, due to the inherent symmetry in such a structure, the contribution due to thermoelectric effects on programming currents…

Compute-In-Memory (CIM) systems, particularly those utilizing ReRAM and memristive technologies, offer a promising path toward energy-efficient neural network computation. However, conventional quantization and compression techniques often…

Nowadays, improving the energy efficiency of high-performance computing (HPC) systems is one of the main drivers in scientific and technological research. As large-scale HPC systems require some fault-tolerant method, the opportunities to…

Compute in-memory (CIM) is a promising technique that minimizes data transport, the primary performance bottleneck and energy cost of most data intensive applications. This has found wide-spread adoption in accelerating neural networks for…

Neuromorphic computing using spin waves is promising for high-speed nanoscale devices, but the realization of high performance has not yet been achieved. Here we show, using micromagnetic simulations and simplified theory with response…

We introduce and experimentally validate a new macro-level model of the CPU temperature/power relationship within nanometer-scale application processors or system-on-chips. By adopting a holistic view, this model is able to take into…

Estimates of energy usage in layers of computing from devices to algorithms have been determined and analyzed. Building on the previous analysis [3], energy needed from single devices and systems including three large-scale computing…

3-D cross point phase change memory (PCM) is a promising emerging memory. However, dynamic performances of 3-D cross point PCM are limited and the role of bias scheme is unknown. Previous studies on bias schemes for planar memories use…

Magnetic random access memory (MRAM) is a leading emergent memory technology that is poised to replace current non-volatile memory technologies such as eFlash. However, the scaling of MRAM technologies is heavily affected by…

As a result of suppressed phonon conduction, large improvements of the thermoelectric figure of merit, ZT, have been recently reported for nanostructures compared to the raw materials' ZT values. It has also been suggested that low…

Phase change memories (PCM) is an emerging type of non-volatile memory that has shown a strong presence in the data-storage market. This technology has recently attracted significant research interest in the development of non-Von Neumann…

Phase change material based thermal energy storage has many current and potential applications in the heating and cooling of buildings, battery and electronics thermal management, thermal textiles, and dry cooling of power plants. However,…

We have measured the critical phase change conditions induced by electrical pulses in Ge2Sb2Te5 nanopillar phase change memory devices by constructing a comprehensive resistance map as a function of pulse parameters (width, amplitude and…