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Correlated Non-Return-to-Zero (CNRZ) signaling exhibits better pin-efficiency compared to the conventional binary differential NRZ signaling, while it does not compromise the sensitivity to Inter-Symbol Interference (ISI). This article analyzes performance of CNRZ transceivers, and provides experimental data for an Ultra-Short Reach (USR) link at 20.83 Gb/s/wire, implemented in FinFET 16 nm technology, consuming 1.02 pJ/b, As CNRZ is based on an orthogonal transformation, both encoding and decoding can be performed in analog, without any cost in terms of latency.

A 1.02pJ/b USR link carrying 416.67 Gb/s/mm die edge (500Gb/s aggregated data rate) in 16nm FinFET, while occupying 2.4mm2, is presented. To enable dense routing over conventional package material, a modified correlated NRZ signaling with low sensitivity to ISI, Xtalk, and common-mod noise has been developed. A matched CTLE/slicer topology has been employed to enhance robustness of the receiver over PVT. A very wideband Rx PLL tracks the majority of Tx jitter, resulting in significant power saving by relaxing Tx design constraints.

This paper describes an algorithm for robotic motion planning that is capable of optimising several cost functions simultaneously to provide optimised, feasible and collision-free paths. The algorithm is based on the best-first graph search algorithm using a Pareto frontier to evaluate costs at each node. Additionally, we include a calculation of the distribution of robot trajectories when the path is realised using a LQR based controller. This ensures that the possibility of collisions is greatly reduced. Results are provided that show multi-cost robotic path planning under position uncertainty and control constraints whilst simultaneously optimising distance travelled and fuel spent. I. INTRODUCTION When creating a motion plan for a robotic vehicle, it is desirable to choose the shortest path to ensure quick execution of the mission. It is also desirable to ensure the vehicle can localise itself with enough precision such that it does not collide with objects in the environment. To achieve this, the vehicle may have to deviate from the obvious shortest path solution. For example, a robot may need to move into open space to obtain a good GPS fix. Furthermore, a robot may have other constraints such as the fuel remaining, shock loading, weather conditions or daylight available that could have an impact on it completing the mission. It is therefore necessary to optimise a motion plan that considers all mission constraints as costs. We therefore propose that a multi-dimensional cost function is required to generate feasible and optimised paths for robotic vehicles to maximise the mission success. Robotic path planning research has traditionally focussed

This paper describes the architecture of a "state of the art" scalable DSP processing system on a single chip. The chip is called the Multimedia Video Processor (MVP) and is the performance flagship of the Texas Instruments TMS320 DSP processor family. This chip utilizes an enhanced 0.5 /spl mu/m triple-level metal 16M DRAM CMOS process. This highly programmable chip, which can sustain over two billion RISC-like operations per second will be used in systems requiring combinations of graphics acceleration, image processing, encoding/decoding compression processing, for example JPEG, MPEG (audio and video), P/spl times/64 standards as well as proprietary compression algorithms. The MVP creates a new frontier of applications for DSP processing. >

Nested loops, usually found within the multi-dimensional (MD) computation problems, can be modeled as MD data flow graphs (MDFGs). In order to optimize such loops, a scheduling technique able to achieve parallel execution in the loop body is required. This paper presents a software system, Precise-MD, designed to solve such problems. Precise-MD allows the user to input the MD problems represented by MDFGs through a graphical interface and then applies an MD-Scheduling algorithm, OPTIMUS, which is able to obtain the shortest schedule length for a resource constrained system in polynomial time. Experiment results demonstrate the usability and application of this tool.