IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Dynamic graph convolutional networks (DGCNs) have emerged as an effective approach to analyzing graph data that is constantly changing. The typical DGCNs incorporate not only graph convolutional networks (GCNs) to extract the structural information but ...

Graph convolutional networks (GCNs) have become a powerful deep learning approach for graph-structured data. Different from traditional neural networks such as convolutional neural networks, GCNs handle irregular input graph data, and GCNs are both ...

Nowadays, field programmable gate arrays (FPGAs) have been widely adopted to serve as accelerators in artificial intelligence and big data related applications. Since the static random access memory (SRAM)-based FPGA is suffering from limited density and ...

High-bandwidth memory (HBM) offers breakthrough memory bandwidth through its vertically stacked memory architecture and through-silicon via (TSV)-based fast interconnect. However, the stacked architecture leads to high-power density causing thermal issues ...

Energy harvesting is increasingly used as a long-term energy supply for the Internet of Things, wireless sensor networks, and cyber-physical systems. However, the challenge of mitigating the variability of energy harvesting sources needs to be addressed ...

Resistive random-access memory (ReRAM)-based manycore architectures enable acceleration of graph neural network (GNN) inference and training. GNNs exhibit characteristics of both DNNs and graph analytics. Hence, GNN training/inferencing on ReRAM-based ...

Developing toward battery-less, energy-harvesting designs is a promising direction for sensor-scale devices. The recent introduction of byte-addressable, nonvolatile memory (NVRAM) enables intermittent computing, which preserves as much program progress ...

Reservoir computing (RC) is attracting attention as a machine-learning technique for edge computing. In time-series classification tasks, the number of features obtained using a reservoir depends on the length of the input series. Therefore, the features ...

A hardware Trojan (HT) is a malicious modification of the design done by a rogue employee or a malicious foundry to leak secret information, create a backdoor for attackers, alter functionality, degrade performance and even halt the system. In Black-hat ...

Guaranteeing timing constraints for parallel real-time applications deployed on multicore platforms is challenging, especially for applications containing non-preemptive execution blocks, that suffer from priority inversions. In this article, we propose ...

Emerging embedded systems, such as autonomous vehicles, demand highly efficient remote data transfer, whereas existing networking hardware and protocols cause high communication latency and CPU consumption. In this article, we propose embedded RDAC (eRDAC)...

Emerging autonomous vehicles equip multiple high-throughput sensors to enable automatic driving, such as multiline lidars and high-definition cameras. Existing automotive embedded systems usually employ software stacks to receive and preprocess high-...

As the mainstream solid-state storage technology, NAND flash has the advantages of tiny size, cost-effective, and high performance, which make it a promising candidate to be embedded into the shingled magnetic recording (SMR) disk to build a faster, ...

Internet of Things (IoT) devices are gradually adopting battery-less, energy harvesting solutions, thereby driving the development of an intermittent computing paradigm to accumulate computation progress across multiple power cycles. While many attempts ...

Side-channel attacks are powerful attacks for retrieving secret data by exploiting physical measurements, such as power consumption or electromagnetic emissions. Masking is a popular countermeasure as it can be proven secure against an attacker model. In ...

Pruning deep neural networks (DNNs) can reduce the model size and thus save hardware resources of a resistive-random-access-memory (ReRAM)-based DNN accelerator. For the tightly coupled crossbar structure, existing ReRAM-based pruning techniques prune the ...

Modern reduced instruction set computer processors are based on a load/store architecture, where all computations are performed on register operands. Compilers therefore allocate registers based on demand, and when occupancy is at maximum, register ...

Spiking neural networks (SNNs) compute in an event-based manner to achieve a more efficient computation than standard neural networks. In SNNs, neuronal outputs are not encoded as real-valued activations but as sequences of binary spikes. The motivation ...

Secure deletion in flash-based storage is crucial for data security. However, existing secure deletion schemes for flash memory suffer from performance degradation and reliability issues and cannot provide secure deletion guarantees. Although emerging ...

The advance of nonvolatile memory in storage technology has presented challenges in redefining the ways in handling the main memory and the storage. This work is motivated by the strong demands in effective handling of page faults over ultralow-latency ...

Near-data processing (NDP) architecture is promised to break the bottleneck of data movement in many scenarios (e.g., databases and recommendation systems), which limits the efficiency of data processing. Different from traditional SSD, NDP-based SSD not ...

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 ...

Process variations and device aging impose profound challenges for circuit designers. Without a precise understanding of the impact of variations on the delay of circuit paths, guardbands, which keep timing violations at bay, cannot be correctly ...

Deep neural networks (DNNs) are being heavily utilized in modern applications, putting energy-constraint devices to the test. To bypass high energy consumption issues, approximate computing has been employed in DNN accelerators to balance out the accuracy-...

Battery-powered devices commonly use Li-ion batteries due to their high energy density. Unfortunately, low ambient temperature and high discharge current significantly affect the available capacity of Li-ion batteries at runtime. One solution to handle ...

Intelligent speech recognition is increasingly used in embedded systems, which is also seriously threatened by malicious speech spoofing attacks. Different from the conventional methods, this article proposes a segment-based anti-spoofing detection (SASD) ...

Many modern real-time parallel applications can be modeled as a directed acyclic graph (DAG) task. Recent studies show that the worst-case response time (WCRT) bound of a DAG task can be significantly reduced when the execution order of the vertices is ...

This article considers a setting where embedded devices are used to acquire and classify images. Because of limited computing capacity, embedded devices rely on a parsimonious classification model with uneven accuracy. When local classification is deemed ...

Safety and robustness properties are highly required for neural networks deployed in safety-critical applications. Current complete verification techniques of these properties suffer from the lack of efficiency and effectiveness. In this article, we ...

Detecting actions in videos have been widely applied in on-device applications, such as cars, robots, etc. Practical on-device videos are always untrimmed with both action and background. It is desirable for a model to both recognize the class of action ...