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Emerging resistive random-access memory (ReRAM) based processing-in-memory (PIM) accelerators have been increasingly explored in recent years because they can efficiently perform in-situ matrix-vector multiplication (MVM) operations involved in a wide ...

Data-driven applications are increasingly central to our information technology society, propelled by AI techniques reshaping various sectors of our economy. Despite their transformative potential, these applications demand immense data processing, ...

ReRAM-based Processing-In-Memory (PIM) architectures have been increasingly explored to accelerate various Deep Neural Network (DNN) applications because they can achieve extremely high performance and energy-efficiency for in-situ analog Matrix-Vector ...

Most existing architectural studies on ReRAM-based processing-in-memory (PIM) DNN accelerators assume that all weights of the DNN can be mapped to the crossbar at once. However, these studies are over-idealized. ReRAM crossbar resources for calculation ...

Spiking neural networks (SNNs) are considered as energy-efficient alternatives to deep neural networks (DNNs). By adopting event-driven information processing, SNNs can significantly reduce the computational demands associated with DNNs, while still ...

Transformers have revolutionized deep learning and generative modeling to enable unprecedented advancements in natural language processing tasks and beyond. However, designing hardware accelerators for executing transformer models is challenging due to ...

Bit-level sparsity in neural network models harbors immense untapped potential. Eliminating redundant calculations of randomly distributed zero-bits significantly boosts computational efficiency. Yet, traditional digital SRAM-PIM architecture, limited by ...

Video analytics has a wide range of applications and has attracted much interest over the years. While it can be both computationally and energy-intensive, video analytics can greatly benefit from in/near memory compute. The practice of moving compute ...

Discrete Hadamard transform (DHT) is a signal processing tool that decomposes an arbitrary input vector into a superposition of Walsh functions. Due to its wide range of applications in processing big data, a fast and energy-efficient hardware ...

• Hadamard Transform.
• Memristor.
• ReRAM.
• PIM.
• Parallel Computing.

• Onboard software (OBSW) of HERCCULES mission that characterize the convective heat and radiative environment in the stratosphere.
• Commercial off-the-shelf hardware and software using a Raspberry Pi 4B as an OBC with Linux OS and the ...

Stratospheric balloons have emerged as an affordable and flexible alternative to traditional spacecrafts as they are implemented using commercial off-the-shelf (COTS) equipment without following strict methodologies. HERCCULES is a stratospheric ...

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The constrained layer damping structure is used to resist the impact damage from blast load and furthermore the two-scale topology optimization of the damping layer can deduce the additional mass of micro damping material while satisfying the ...

Motivated by reducing the data transfer activities in data-intensive neural network computing, SRAM-based compute-in-memory (CiM) has made significant progress. Unfortunately, SRAM has low density and limited on-chip capacity. This makes the deployment ...

As one of the cornerstones of computing, dynamic random-access memory (DRAM) is prevalent across digital systems. Over the years, researchers have proposed modifications to DRAM macros or explored alternative uses of existing DRAM chips to extend the ...

This document defines a YANG data model that can be used to configure and manage devices supporting Protocol Independent Multicast (PIM). The model covers the PIM protocol configuration, operational state, and event notifications data.

In recent years, the limits of the multicore approach emerged in the so-called “dark silicon” issue and diminishing returns of an ever-increasing core count. Hardware manufacturers, out of necessity, switched their focus to ...

Processing-in-memory (PIM) is a computational architecture in which the processing unit and memory are integrated into a single unit. Different technologies and methods can be used to implement PIM, but a more optimal design for PIM can be ...

Graph Neural Networks (GNNs) have both graph processing and neural network computational features. Traditional graph accelerators and NN accelerators cannot meet these dual characteristics of GNN applications simultaneously. In this work, we propose a ...

Recently, we are witnessing a surge in DRAM-based Processing in Memory (PIM) publications from academia and industry. The architectures and design techniques proposed in these publications vary largely, ranging from integration of computation ...

Processing-in-memory (PIM) minimizes data movement overheads by placing processing units near each memory segment. Recent PIMs employ processing units with a SIMD architecture. However, kernels with random accesses, such as sparse-matrix-dense-vector (...

JPEG is a common encoding format for digital images. Applications that process large numbers of images can be accelerated by decoding multiple images concurrently. We examine the suitability of using a large array of in-memory processors (PIM) to obtain ...