The Cerebras Wafer-Scale Engine (WSE) delivers performance at an unprecedented scale of over 900,000 compute units, all connected via a single-wafer on-chip interconnect. Initially designed for AI, the WSE architecture is also well-suited for High Performance Computing (HPC). However, its distributed asynchronous programming model diverges significantly from the simple sequential or bulk-synchronous programs that one would typically derive for a given mathematical program description. Targeting the WSE requires a bespoke re-implementation when porting existing code. The absence of WSE support in compilers such as MLIR, meant that there was little hope for automating this process.

The goal of this paper is to propose a new approach for controlling smart home devices using physiological and movements signals (electromyogram, accelerometer and gyroscope). Our proposed approach exploits the ElectroMyoGram (EMG) signal to detect user muscles contraction and to trigger the associated actions on the controlled device. The triggered action is based on the position of the hand or its movement pattern. The Support Vector Machine (SVM) is used to classify the gyroscope data and to detect hand movements. The position of the hand is determined using the K-Nearest Neighbors (KNN) algorithm. Our proposed approach can be used to command any electronic device or connected objects and it is intended to work with data from wearable arm band containing triaxial accelerometer, gyroscope and able to measure EMG signal. Our experimental results are very encouraging where we achieve fast processing and reliable evaluation of hand movement with very low rate of actions miss interpretation.

Chip manufacturers continue to increase the number of cores per chip while balancing requirements for low power consumption. This drives a need for simpler cores and hardware caches. Because of these trends, the scalability of existing shared memory system software is in question. Traditional operating systems (OS) for multiprocessors are based on shared memory communication between cores and are symmetric (SMP). Contention in SMP OSes over shared data structures is increasingly significant in newer generations of many-core processors. We propose the use of the replicated-kernel OS design to improve scalability over the traditional SMP OS. Our replicated-kernel design is an extension of the concept of the multikernel. While a multikernel appears to application software as a distributed network of cooperating micro kernels, we provide the appearance of a monolithic, single-system image, task-based OS in which application software is unaware of the distributed nature of the underlying OS. In this paper we tackle the problem of thread migration between kernels in a replicated-kernel OS. We focus on distributed thread group creation, context migration, and address space consistency for threads that execute on different kernels, but belong to the same distributed thread group. This concept is embodied in our prototype OS, called Popcorn Linux, which runs on multicore x86 machines and presents a Linux-like interface to application software that is indistinguishable from the SMP Linux interface. By doing this, we are able to leverage the wealth of existing Linux software for use on our platform while demonstrating the characteristics of the underlying replicated-kernel OS. We show that a replicated-kernel OS scales as well as a multikernel OS by removing the contention on shared data structures. Popcorn, Barr elfish, and SMP Linux are compared on selected benchmarks. Popcorn is shown to be competitive to SMP Linux, and up to 40% faster.