ACM Transactions on Design Automation of Electronic Systems

Graph application workloads are dominated by random memory accesses with the poor locality. To tackle the irregular and sparse nature of computation, ReRAM-based Processing-in-Memory (PIM) architectures have been proposed recently. Most of these ReRAM ...

Efficient and adaptive computer vision systems have been proposed to make computer vision tasks, such as image classification and object detection, optimized for embedded or mobile devices. These solutions, quite recent in their origin, focus on ...

Energy efficiency has become the new performance criterion in this era of pervasive embedded computing; thus, accelerator-rich multi-processor system-on-chips are commonly used in embedded computing hardware. Once computationally intensive machine ...

Today, as deep learning (DL) is applied more often in daily life, dedicated processors such as CPUs and GPUs have become very important for accelerating model executions. With the growth of technology, people are becoming accustomed to using edge devices, ...

The emerging non-volatile memory (eNVM) based mixed-signal Compute-in-Memory (CIM) accelerators are of great interest in today's AI accelerators design due to their high energy efficiency. Various CIM architectures and circuit-level designs have been ...

With the popularity of deep learning, the hardware implementation platform of deep learning has received increasing interest. Unlike the general purpose devices, e.g., CPU or GPU, where the deep learning algorithms are executed at the software level, ...

Convolution neural networks (CNNs) are widely used algorithms in image processing, natural language processing and many other fields. The large amount of memory access of CNNs is one of the major concerns in CNN accelerator designs that influences the ...

The demand for single ended static random access memory is growing, driven by the decreasing technology node and increasing processing load. This mandates the need for a single ended sense amplifier to be used along with the memory. Consequently, a single ...

Neural Network (NN)-based real-time inferencing tasks are often co-scheduled on GPGPU-style edge platforms. Existing works advocate using different NN parameters for the same detection task in different environments. However, realizing such approaches ...

The fault diagnosability of a network indicates the self-diagnosis ability of the network, thus it is an important measure of robustness of the network. As a neoteric feature for measuring fault diagnosability, the r-component diagnosability ct_r(G) of a ...

Convolution Neural Networks (CNNs) are widely deployed in computer vision applications. The datasets are large, and the data reuse across different parts is heavily interleaved. Given that memory access (SRAM and especially DRAM) is more expensive in both ...

Megatrends such as Highly Automated Driving (HAD) (SAE ≥ Level 3), electrification, and connectivity are reshaping the automotive industry. Together with the new technologies, the business models will also evolve, opening up new possibilities and new ...

Image and video processing algorithms are currently crucial for many applications. Hardware implementation of these algorithms provides higher speed for large computation applications. Removing noise is often a typical pre-processing step to enhance the ...

The computing capacity demanded by embedded systems is on the rise as software implements more functionalities, ranging from best-effort entertainment functions to performance-guaranteed safety-related functions. Heterogeneous manycore processors, using ...