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Computationally expensive, high-accuracy detector simulations are a major bottleneck for many particle physics experiments such as those at the Large Hadron Collider (LHC) as well as those planned for future colliders. This challenge has…

A practical deep neural network's (DNN) evaluation involves thousands of multiply-and-accumulate (MAC) operations. To extend DNN's superior inference capabilities to energy constrained devices, architectures and circuits that minimize…

A line driver with configurable pre-emphasis is implemented in a 65 nm CMOS process. The driver utilizes a three-tap feed-forward equalization (FFE) architecture. The relative delays between the taps are selectable in increments of 1/16th…

This paper addresses delay test for SOC devices with high frequency clock domains. A logic design for on-chip high-speed clock generation, implemented to avoid expensive test equipment, is described in detail. Techniques for on-chip clock…

High penetration of photovoltaic (PV) generators can lead to voltage issues in distribution networks. Various approaches including the real power control through PV inverters have been proposed to address voltage issues. However, among…

Residual neural networks are widely used in computer vision tasks. They enable the construction of deeper and more accurate models by mitigating the vanishing gradient problem. Their main innovation is the residual block which allows the…

This paper proposes a new coil configuration that uses solenoid ferrites on the receiver side instead of planar ferrite coils that are employed in existing wireless charging systems.The solenoid ferrites are used in the drone legs to help…

Synthetic aperture radar (SAR) automatic target recognition (ATR) is the key technique for remote-sensing image recognition. The state-of-the-art convolutional neural networks (CNNs) for SAR ATR suffer from \emph{high computation cost} and…

Convolutional neural network (CNN) accelerators implemented on Field-Programmable Gate Arrays (FPGAs) are typically designed with a primary focus on maximizing performance, often measured in giga-operations per second (GOPS). However,…

Deep neural network (DNN) inference relies increasingly on specialized hardware for high computational efficiency. This work introduces a field-programmable gate array (FPGA)-based dynamically configurable accelerator featuring systolic…

In recent years deep learning algorithms have shown extremely high performance on machine learning tasks such as image classification and speech recognition. In support of such applications, various FPGA accelerator architectures have been…

We have designed, implemented and tested a time-to-digital converter core in a low-cost Spartan-6 FPGA. Our design exploits the finite propagation speed in carry chains to realize a delay line in which the propagation distance of the…

Next-generation cellular systems require low-power mm-wave phased-array ICs. Variable-gain LNAs (VG-LNAs) are key blocks enabling reduced hardware complexity, performance improvement and added functionalities. This paper reports a low-power…

A 3.8ps root mean square (RMS) time synchronization implemented in a 20nm fabrication process ultrascale kintex Field Programmable Gate Array (FPGA) is presented. The multichannel high-speed serial transceivers (e.g. GTH) play a key role in…

Contemporary field-programmable gate arrays (FPGAs) are predestined for the application of finite impulse response (FIR) filters. Their embedded digital signal processing (DSP) blocks for multiply-accumulate operations enable efficient…

This paper presents the design and implementation of a high-density, deterministic trigger distribution system tailored for the C-band photocathode electron gun test platform at the Southern Advanced Photon Source (SAPS). Implemented within…

This paper outlines an FPGA VLSI design methodology that was used to realize a fully functioning FPGA chip in 130nm CMOS with improved routability and memory robustness. The architectural design space exploration and synthesis capability…

We report on a traceable calibration system for a 3500mm-long console that carries a measurement system for inspecting the diameter of a circular reactor chassis. The system uses two single-pass laser interferometers with homodyne fringe…

Deep Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in a wide range of applications. However, deeper CNN models, which are usually computation consuming, are widely required for complex Artificial…

Low-latency, energy-efficient deep neural networks (DNNs) inference are critical for edge applications, where traditional cloud-based deployment suffers from high latency and security risks. Field-Programmable Gate Arrays (FPGAs) offer a…