The resource requirements of deep neural networks (DNNs) pose significant challenges to their deployment on edge devices. Common approaches to address this issue are pruning and mixed-precision quantization, which lead to latency and memory occupation ...

As a new computing paradigm, hyperdimensional computing (HDC) has gradually manifested its advantages in edge-side intelligent applications by virtue of its interpretability, hardware-friendliness and robustness. The core of HDC is to encode input samples ...

Hardware-managed tagged memory is the dominant way of supporting tags in current processor designs. Most of these processors reserve a hidden tag partition in the memory dedicated for tags and use a small tag cache (TC) to reduce the extra memory accesses ...

In-memory Computing (IMC) using Phase Change Memory (PCM) has proven to be effective for efficient processing of Deep Neural Networks (DNNs). However, with the use of multi-level cell PCM (MLC-PCM) in NVMs-based accelerators, errors due to resistance ...

Persistent memory (PM) is promising to be the next-generation storage device with better I/O performance. Since the traditional I/O path is too lengthy to drive PM featuring low latency and high bandwidth, prior works proposed memory-mapped I/O (MMIO) to ...

Among several emerging architectures, computing in memory (CIM), which features in-situ analog computation, is a potential solution to the data movement bottleneck of the Von Neumann architecture for artificial intelligence (AI). Interestingly, more ...

Unary computing is a relatively new method for implementing arbitrary nonlinear functions that uses unpacked thermometer number encoding, enabling much lower hardware costs. In its original form, unary computing provides no trade-off between accuracy and ...

Phase Change Memory (PCM) is considered one of the most promising scalable non-volatile main memory alternatives to DRAM. It provides <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><mml:math><mml:mo>&#x0223C;</mml:mo></mml:math><...

Graph random walk sampling is becoming increasingly important with the widespread popularity of graph applications. It aims to capture the desirable graph properties by launching multiple walkers to collect feature paths. However, previous research ...

Approximate multipliers (AMs) have widely been investigated to pursue high-performance and energy-efficient hardware designs for error-tolerant applications, such as neural networks (NNs). The computing accuracy of an AM has been evaluated by using ...

In-memory ordered key-value stores are an important building block in modern distributed applications. We present Honeycomb, a hybrid software-hardware system for accelerating read-dominated workloads on ordered key-value stores that provides ...

Due to the strong demand of massive storage capacity, the density of flash memory has been improved in terms of technology node scaling, multi-bit per cell technique, and 3D stacking. However, these techniques also degrade read performance and ...

Instruction prefetching is instrumental for guaranteeing a high flow of instructions through the processor front end for applications whose working set does not fit in the lower-level caches. Examples of such applications are server workloads, whose ...

Bayesian inference is one method of statistical inference in machine learning. It predicts the probability that a given test belongs to a certain class and is widely used in various applications such as medical diagnosis, spam classification and fraud ...

Data transfers are essential in today&#x0027;s computing systems as latency and complex memory access patterns are increasingly challenging to manage. Direct memory access engines (DMAES) are critically needed to transfer data independently of the ...

With the popularity of cloud services, cloud block storage (CBS) systems have been widely deployed by cloud providers. Cloud cache plays a vital role in maintaining high and stable performance in cloud block storage systems. In the past few decades, much ...

Facing the threat of large-scale quantum computers to traditional public-key cryptography, the National Institute of Standards and Technology has conducted Post-Quantum Cryptography algorithms evaluation for a long time, and CRYSTALS-Kyber has been ...

Shared L1 memory clusters are a common architectural pattern (e.g., in GPGPUs) for building efficient and flexible multi-processing-element (PE) engines. However, it is a common belief that these tightly-coupled clusters would not scale beyond a few tens ...

Division and square root are fundamental operations required by most computer systems. They are commonly implemented in hardware using radix-4 recurrence, which produces a 2-bit result digit on each step. Unified digit selection logic chooses the next ...

In-memory computing is an emerging computing paradigm to breakthrough the von-Neumann bottleneck. The SRAM based in-memory computing (SRAM-IMC) attracts great concerns from industries and academia, because the SRAM is technology compatible with the widely-...