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Blockchain systems play an important role in distributed ledgers, database systems, etc. As more and more blocks are mined, the storage burden of blockchain system is significantly increased. The current blockchain system uniformly transforms all its data ...

Non-volatile processors are proposed for ambient energy harvesting systems to enable accumulative computing across power failures. They employ nonvolatile memory for processor status backup before power outage and resume the system after power recovers. A ...

The Log-Structure Merged tree (LSM-tree) based key-value (KV) store has been widely adopted as the storage engine for blockchain systems, such as Ethereum, in which blockchain data are uniformly transformed into randomly distributed KV items for ...

Parkinson’s disease (PD) is a neurodegenerative disease that causes a movement disorder. Early diagnosis of PD is critical for patients to receive proper treatment, such as levodopa/carbidopa, which are more effective when administered early on at ...

With the increment of data sets and models sizes, distributed deep learning has been proposed to accelerate training and improve the accuracy of DNN models. The parameter server framework is a popular collaborative architecture for data-parallel training, ...

To support nonsequential writes, persistent cache (PC) is constructed in drive managed SMR (DM-SMR) drive. However, PC cleaning introduces drastic performance degradation and enlarges tail latencies. In this article, we propose to utilize reinforcement ...

Log-structured merge (LSM) tree based key-value (KV) stores organize writes into hierarchical batches for high-speed writing. However, the notorious compaction process of LSM-tree severely hurts system performance. It not only involves huge I/O ...

Pruning and quantization are two efficient techniques to achieve performance improvement and energy saving for ReRAM-based DNN accelerators. However, most existing ReRAM-based DNN accelerators using pruning and quantization are based ...

Blockchain has been widely deployed in various fields, such as finance, education, and public services. Blockchain has decentralized mechanisms with persistency and auditability and runs as an immutable distributed ledger, where transactions are jointly ...

Deep convolutional neural networks (DCNNs) are one of the most promising models for pattern recognition and classification tasks. With the development of wearable devices and the Internet of Things (IoTs), integrating DCNNs onto ...

Benefiting from the characteristics of non-volatile, low leakage power consumption, high density, and good scalability, resistive random access memory (ReRAM) has been considered to replace DRAM as the main memory of embedded devices. ...

DM-SMR adopts Persistent Cache (PC) to accommodate non-sequential write operations. However, the PC cleaning process induces severe long-tail latency. In this paper, we propose to mitigate the tail latency of PC cleaning by using Reinforcement Learning (...

Deep convolutional neural networks (DCNNs) have achieved great success in various applications. Nevertheless, training and deploying such DCNNs models require a huge amount of computation and storage resources. Weight pruning has emerged as an ...$\mathrm{}$

Motion estimation (ME) is a high efficiency video coding (HEVC) process for determining motion vectors that describe the blocks transformation direction from one adjacent frame to a future frame in a video sequence. ME is a memory and ...

Recently, federated learning has attracted great attention due to its advantage of enabling model training in a distributed manner. Instead of uploading data for centralized training, it allows devices to keep local data private and ...

Field Programmable Gate Array (FPGA) platform has been a popular choice for deploying Convolution Neural Networks (CNNs) as a result of its high parallelism and low energy consumption. Due to the limited on-chip computation and storage resources, FPGA ...

Since static random access memory (SRAM)-based field-programmable gate array (FPGA) has limited density and comparatively high leakage power, researchers have proposed FPGA architectures based on emerging nonvolatile memories (NVMs) to satisfy the ...

Motion estimation (ME) is an HEVC process for determining motion vectors that describe the blocks transformation direction from one frame to a future adjacent frame in a video sequence. ME is a memory and computationally intensive process which ...

Weight sparsity has been explored to achieve energy efficiency for Resistive Random-access Memory (ReRAM) based DNN accelerators. However, most existing ReRAM-based DNN accelerators are based on an overidealized crossbar architecture and mainly focus on ...

Static random access memory– (SRAM) based field programmable gate arrays (FPGAs) are currently facing challenges of limited capacity and high leakage power. To solve this problem, non-volatile memory (NVM) is proposed as the alternative to build non-...