Wearable and Internet of Things (IoT) devices are becoming popular in several applications, such as health monitoring, wide area sensing, and digital agriculture. These devices are energy-constrained due to limited battery capacities. As such, IoT devices ...

In the era of big data, social network services continuously modify social connections, leading to dynamic and evolving graph data structures. These evolving graphs, vital for representing social relationships, pose significant memory challenges as they ...

Fully homomorphic encryption (FHE) offers a promising solution to ensure data privacy by enabling computations directly on encrypted data. However, its notorious performance degradation severely limits the practical application, due to the explosion of ...

Key-value stores (KVStores), such as LevelDB and Redis, have been widely used in real-world production environments. To guarantee data durability and availability, traditional KVStores suffer from high write latency, mainly caused by the long network and ...

Hardware data prefetching is a well-studied technique to bridge the processor-memory performance gap. Bit-pattern-based prefetchers are one of the most promising spatial data prefetchers that achieve substantial performance gains. In bit-pattern-based ...

Block-circulant matrix (BCM) compression has garnered much attention in the hardware acceleration of convolutional neural networks (CNNs) due to its regularity and efficiency. However, constrained by the difficulty of exploring the compression parameter ...

Nonvolatile memory (NVM) suffers from more serious nonuniform memory access (NUMA) effects than DRAM because of the lower bandwidth and higher latency. While numerous works have aimed at optimizing NVM indexes, only a few of them tried to address the NUMA ...

This article presents an area-efficient Barrett modular multiplication (BMM) algorithm, facilitating the development of cryptosystems like fully homomorphic encryption. Instead of implementing three normal multiplications required by classic BMM, our ...

With the drive toward ever more intelligent devices, neural networks (NNs) are deployed on smaller and smaller systems. For these embedded microcontrollers, memory consumption becomes a significant challenge. We propose multiple encoding schemes that ...

Quantum network coding (QNC) is expected to enable multiple quantum data packets to be fused before the transmission via the bottleneck channel in order to improve the transmission efficiency and the total throughput of quantum networks. Although several ...

Protection of integrated circuits (ICs) against hardware security threats has been tackled by many schemes proposed to mitigate risks associated with an unauthorized access and usage of ICs in general, and intellectual property (IP) cores in particular. ...

Top-K sparsification-based compression techniques are popular and powerful for reducing communication costs in federated learning (FL). However, existing Top-K sparsification-based compression methods suffer from two critical issues that severely hinder ...

Distributed test data compression refers to the scenario where each logic block in a design has its own decompression logic and compact set of compressed tests. A static test compaction procedure for this scenario was described recently. The procedure ...

Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks. However, the prohibitive computational cost of CNNs hinders the deployment of CNNs onto resource-constrained embedded devices. To address this issue, ...

Emerging persistent memory (PM) has the potential to substitute DRAM due to its near-DRAM performance and durability similar to disks. However, hash tables designed for DRAM cannot be directly adopted for PM. Moreover, prior studies on hash tables using ...

Recently, deep convolutional neural networks (CNNs) have achieved eye-catching results in various applications. However, intensive memory access of activations introduces considerable energy consumption, resulting in a great challenge for deploying CNNs ...

The advancement of nonvolatile memory (NVM) technology reduces the cost-per-unit of solid-state drives (SSDs). Flash memory-based SSDs have become ubiquitous because they provide better performance and energy efficiency than hard disk drives. However, it ...

Because of large cell density, fast read/write performance, and no limited write cycles, magnetic skyrmion racetrack memory (SK-RM) has been regarded as the next-generation main memory technology. However, the characteristics of SK-RM are not friendly for ...

With the emergence of big data era, NoSQL key-value database is considered as a promising candidate for replacing relational database management system (RDBMS). As cost per GB of flash memory is getting closer to HDD, high performance solid state drive (...

Side-channel attacks are a concrete and practical threat to the security of computing systems, ranging from high performance platforms to embedded devices. In this paper, we will provide a brief systematization of the current existing approaches to ...