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A high efficiency all-CMOS switched-capacitor DC-DC converter (SC DC-DC) with frequency, switch size and interleaving scaling is proposed in order to achieve high efficiency for wireless sensors and internet of things (IOTs) applications. According to the output load current, decision making unit adjusts the frequency, switch size and number of interleavers to match the required current profile and reach steady state. 4-equally-phased clocks are utilized in the design of the SC DC-DC converter. The converter is implemented in 65nm CMOS process with a peak efficiency of 85% at 600A load current. It can supply output current ranging from 20A to 5mA with efficiency higher than 75%. The converter can be configured into 3 different gain topologies to support nominal output voltages of 1.1V, 1.65V and 2.2V, from a 3.3V input supply. Total area of the converter is 0.725mm2.

Network latency is an important metric for many networked systems. For small-scale systems, explicit measurements are carried out to collect $N \times (N-1)$ latency values to cover any pairs of nodes in the network. But this is not practical for large-scale systems due to the significant traffic and processing overhead needed for actual end-to-end latency measurements. Therefore, instead of actual measurements, researchers have proposed to estimate the round-trip times (RTT) to predict the latencies between all nodes within a network based on a small set of actual RTT measurements. However, such methods not only assume that the network is symmetric, which is not necessarily the case in reality, but also require time to converge. In this work, we present a novel method of network latency estimation using Artificial Intelligence (AI), specifically machine learning, which not only does not require any explicit measurements, but is also drastically faster than existing methods. Our method is trained using the well-known iConnect-Ubisoft dataset of actual RTT measurements, and uses the IP address as the primary input. Performance evaluations using two different datasets show that 73.6% and 59.3% of the measurements, respectively for each dataset, are within 20% estimation error.

Network latency is an important metric specially for distributed systems. Depending on the system size, network latency can be either explicitly measured or predicted. However, prediction methods suffer from several drawbacks which lead to poor performance. The goal of this study is to demonstrate a novel method of network latency estimation which will be considered a valuable addition to the existing works due to its accuracy and efficiency. A number of machine learning techniques such as conventional linear regression, convolutional neural network, and support vector machine are used to predict the value to the end-to-end latency between any given pair of nodes. Two datasets: Ubique and iConnect-Ubisoft are used for training and testing the machine learning algorithms.