Computer Architectures to Close the Loop in Real-time Optimization

© 2015 IEEE.Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as fast optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other

Lagarto I RISC-V Multi-core: Research Challenges to Build and Integrate a Network-on-Chip

Current compute-intensive applications largely exceed the resources of single-core processors. To face this problem, multi-core processors along with parallel computing techniques have become a solution to increase the computational performance. Likewise, multi-processors are fundamental to support new technologies and new science applications challenges. A specific objective of the Lagarto project developed at the National Polytechnic Institute of Mexico is to generate an ecosystem of high-performance processors for the industry and HPC in Mexico, supporting new technologies and scientific applications. This work presents the first approach of the Lagarto project to the design of multi-core processors and the research challenges to build an infrastructure that allows the flagship core of the Lagarto project to scale to multi- and many-cores. Using the OpenPiton platform with the Ariane RISC-V core, a functional tile has been built, integrating a Lagarto I core with memory coherence that executes atomic instructions, and a NoC that allows scaling the project to many-core versions. This work represents the initial state of the design of mexican multi-and many-cores processors

Hardware Acceleration Using Functional Languages

Cílem této práce je prozkoumat možnosti využití funkcionálního paradigmatu pro hardwarovou akceleraci, konkrétně pro datově paralelní úlohy. Úroveň abstrakce tradičních jazyků pro popis hardwaru, jako VHDL a Verilog, přestáví stačit. Pro popis na algoritmické či behaviorální úrovni se rozmáhají jazyky původně navržené pro vývoj softwaru a modelování, jako C/C++, SystemC nebo MATLAB. Funkcionální jazyky se s těmi imperativními nemůžou měřit v rozšířenosti a oblíbenosti mezi programátory, přesto je předčí v mnoha vlastnostech, např. ve verifikovatelnosti, schopnosti zachytit inherentní paralelismus a v kompaktnosti kódu. Pro akceleraci datově paralelních výpočtů se často používají jednotky FPGA, grafické karty (GPU) a vícejádrové procesory. Praktická část této práce rozšiřuje existující knihovnu Accelerate pro počítání na grafických kartách o výstup do VHDL. Accelerate je možno chápat jako doménově specifický jazyk vestavěný do Haskellu s backendem pro prostředí NVIDIA CUDA. Rozšíření pro vysokoúrovňovou syntézu obvodů ve VHDL představené v této práci používá stejný jazyk a frontend.The aim of this thesis is to research how the functional paradigm can be used for hardware acceleration with an emphasis on data-parallel tasks. The level of abstraction of the traditional hardware description languages, such as VHDL or Verilog, is becoming to low. High-level languages from the domains of software development and modeling, such as C/C++, SystemC or MATLAB, are experiencing a boom for hardware description on the algorithmic or behavioral level. Functional Languages are not so commonly used, but they outperform imperative languages in verification, the ability to capture inherent paralellism and the compactness of code. Data-parallel task are often accelerated on FPGAs, GPUs and multicore processors. In this thesis, we use a library for general-purpose GPU programs called Accelerate and extend it to produce VHDL. Accelerate is a domain-specific language embedded into Haskell with a backend for the NVIDIA CUDA platform. We use the language and its frontend, and create a new backend for high-level synthesis of circuits in VHDL.

A Course On Advanced Real-Time Embedded Systems

This thesis discusses the development of an advanced real-time embedded systems course offered at California Polytechnic State University, San Luis Obispo, which aims to prepare students to design modern complex real-time embedded systems. It describes the goals of the real-time embedded systems curriculum, which includes an introductory and advanced course. Finally, this paper discusses the challenges of creating a successful advanced real-time embedded systems course and proposes changes to the current advanced real-time embedded systems course in response to those challenges

Reconfigurable network systems and software-defined networking

Modern high-speed networks have evolved from relatively static networks to highly adaptive networks facilitating dynamic reconfiguration. This evolution has influenced all levels of network design and management, introducing increased programmability and configuration flexibility. This influence has extended from the lowest level of physical hardware interfaces to the highest level of network management by software. A key representative of this evolution is the emergence of softwaredefined networking (SDN). In this paper, we review the current state of the art in reconfigurable network systems, covering hardware reconfiguration, SDN, and the interplay between them. We take a top-down approach, starting with a tutorial on software-defined networks. We then continue to discuss programming languages as the linking element between different levels of software and hardware in the network. We review electronic switching systems, highlighting programmability and reconfiguration aspects, and describe the trends in reconfigurable network elements. Finally, we describe the state of the art in the integration of photonic transceiver and switching elements with electronic technologies, and consider the implications for SDN and reconfigurable network systems.This work was jointly supported by the UKs Engineering and Physical Sciences Research Council (EPSRC) Internet Project EP/H040536/1, an EPSRC Research Fellowship grant to Philip Watts (EP/I004157/2), and DARPA and AFRL under contract FA8750-11-C-0249.This is the final version of the article. It first appeared from IEEE via http://dx.doi.org/10.1109/JPROC.2015.243573