Related papers: STeP-CiM: Strain-enabled Ternary Precision Computa…
Thermal Energy Storage (TES) using Phase Change Materials (PCMs) represents a critical technology for sustainable energy management and grid stability. This study presents a novel Physics-Driven Deep Learning (PDDL) framework for modeling…
With the broad recent research on ferroelectric hafnium oxide for non-volatile memory technology, depolarization effects in HfO2-based ferroelectric devices gained a lot of interest. Understanding the physical mechanisms regulating the…
Although deep learning-based personalized recommendation systems provide qualified recommendations, they strain data center resources. The main bottleneck is the embedding layer, which is highly memory-intensive due to its sparse, irregular…
Stencil computations are a fundamental kernel in scientific computing, critical for simulations in domains such as fluid dynamics and climate modeling. However, these computations are often memory-bound on traditional High-Performance…
The excellent performance of modern deep neural networks (DNNs) comes at an often prohibitive training cost, limiting the rapid development of DNN innovations and raising various environmental concerns. To reduce the dominant data movement…
Prospective spintronic memory and logic devices will benefit from the negligible stray field and ultrafast magnetic dynamics inherent to antiferromagnets [1]. However, realizing isothermal, nonvolatile, and deterministic switching of…
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…
Phase engineering by strains in 2D semiconductors is of great importance for a variety of applications. Here, we present a study of strain induced ferroelectric (FE) transition on bismuth oxyselenide (Bi$_2$O$_2$Se) films, a…
Processing in memory (PiM) represents a promising computing paradigm to enhance performance of numerous data-intensive applications. Variants performing computing directly in emerging nonvolatile memories can deliver very high energy…
Reservoir computing is a brain-inspired machine learning framework for processing temporal data by mapping inputs into high-dimensional spaces. Physical reservoir computers (PRCs) leverage native fading memory and nonlinearity in physical…
A lot of recent progress has been made in ultra low-bit quantization, promising significant improvements in latency, memory footprint and energy consumption on edge devices. Quantization methods such as Learned Step Size Quantization can…
We investigate progressive freezing as an alternative to straight-through estimators (STE) for training binary networks from scratch. Under controlled training conditions, we find that while global progressive freezing works for…
Hyperdimensional (HD) computing involves encoding of baseline information into large hypervectors and repeated Boolean operations to generate the output class hypervectors which are stored in an associative memory. The classification task…
The growing scale of deep learning demands distributed training frameworks that jointly reason about parallelism, memory, and network topology. Prior works often rely on heuristic or topology-agnostic search, handling communication and…
In 1963, Moll and Tarui suggested that the field-effect conductance of a semiconductor could be controlled by the remanent polarization of a ferroelectric (FE) material to create a ferroelectric field-effect transistor (FE-FET). However,…
Resistive Random Access Memory (ReRAM) based Processing In Memory (PIM) Accelerator has emerged as a promising computing architecture for memory intensive applications, such as Deep Neural Networks (DNNs). However, due to its immaturity,…
Processing-in-memory (PIM) architectures bring computation closer to data, reducing the processor-memory transfer bottleneck in traditional processor-centric designs. Novel hardware solutions, such as UPMEM's in-memory processing…
A new mechanism for memristive switching in 2D materials is through electric-field controllable electronic/structural phase transitions, but these devices have not outperformed status quo 2D memristors. Here, we report a high-performance…
Fluorite-type $\mathrm{HfO_2}$-based ferroelectric (FE) oxides have rekindled interest in FE memories due to their compatibility with silicon processing and potential for high-density integration. The polarization characteristics of FE…
The computing wall and data movement challenges of deep neural networks (DNNs) have exposed the limitations of conventional CMOS-based DNN accelerators. Furthermore, the deep structure and large model size will make DNNs prohibitive to…