Quantum computing (QC) is a multidisciplinary field comprising aspects of computer science, physics, and mathematics that utilizes quantum mechanics to solve complex problems faster than on classical computers (e.g., QC promises to process data with estimated speeds exceeding 100 million times relative to classical computers). Signal processing is...

Synchronous logic remains the dominant design paradigm of digital systems such as Application Specific Integrated Circuits (ASICs). The conventional design of sequential circuit networks is based on the assumption that every register receives the clock signal at the same time. However, guaranteeing the simultaneity of clock arrival times in practice...

Traditional von-Neumann computing architectures, such as CPUs and GPUs, demonstrate limitations in memory bandwidth and energy efficiency. However, their high demand lies in their programmability and flexible functionality. Such platforms execute a wide spectrum of bit-wise logic and arithmetic operations. In this regard, recent application-specific...

­Background In the era of artificial intelligence, various deep neural networks (DNNs), such as multi-layer perceptron, convolutional neural networks, and recurrent neural networks, have emerged and achieved human-level performance in many recognition tasks. These DNNs usually require billions of multiply-and-accumulate (MAC) operations, soliciting...

Background The growing market for wearable, portable, and implantable devices has understandably led to increasing demand for low-power, compact electronics. High-speed data transmission requires phase-locked loops (PLLs) capable of carrier frequency generation with extremely low noise. The challenge of balancing oscillator noise performance with area...

In digital systems, the difference in the arrival times at two registers is referred to as the clock skew between those registers. Clock skewing is a widely used optimization technique in conventional application-specific integrated circuit (ASIC) designs and typically implemented by introducing buffers to ensure clocking of components happens in the...

Background Neuro-inspired machine deep learning have demonstrated that they can perform complex tasks such as image and speech classification, object recognition, and real-time decision-making. The state-of-the-art deep learning algorithms employ neural networks which have millions of parameters. Deploying such deep neural networks onto mobile platforms...

Artificial neural networks have countless applications with their ability for adaptive learning, recognition, and classification. When implemented in software, the artificial neural network algorithms often use a double-precision format. This can result in large area and power requirements for the hardware. Other approaches can be used to determine...

The fundamental building blocks of all computer electronics, integrated circuits (ICs) are the least expensive way to create the digital circuits needed for processing binary information. A soft error is when a binary switch within a digital circuit flips to its opposite state and causes an electronic device to malfunction. Modular redundancy is a method...

As a result of inefficiency, radio frequency (RF) power amplifiers typically consume high levels of current. Consequently, there have been numerous attempts to use external circuitry in order to improve the efficiency of RF power amplifiers located on integrated circuits (ICs); however, these attempts have failed to provide the desired improvements...