Towards Efficient Hyperdimensional Computing Using Photonics

Over the past few years, silicon photonics-based computing has emerged as a promising alternative to CMOS-based computing for Deep Neural Networks (DNN). Unfortunately, the non-linear operations and the high-precision requirements of DNNs make it extremely challenging to design efficient silicon photonics-based systems for DNN inference and training. Hyperdimensional Computing (HDC) is an emerging, brain-inspired machine learning technique that enjoys several advantages over existing DNNs, including being lightweight, requiring low-precision operands, and being robust to noise introduced by the nonidealities in the hardware. For HDC, computing in-memory (CiM) approaches have been widely used, as CiM reduces the data transfer cost if the operands can fit into the memory. However, inefficient multi-bit operations, high write latency, and low endurance make CiM ill-suited for HDC. On the other hand, the existing electro-photonic DNN accelerators are inefficient for HDC because they are specifically optimized for matrix multiplication in DNNs and consume a lot of power with high-precision data converters. In this paper, we argue that photonic computing and HDC complement each other better than photonic computing and DNNs, or CiM and HDC. We propose PhotoHDC, the first-ever electro-photonic accelerator for HDC training and inference, supporting the basic, record-based, and graph encoding schemes. Evaluating with popular datasets, we show that our accelerator can achieve two to five orders of magnitude lower EDP than the state-of-the-art electro-photonic DNN accelerators for implementing HDC training and inference. PhotoHDC also achieves four orders of magnitude lower energy-delay product than CiM-based accelerators for both HDC training and inference

The shipper’s perspective on distance and time and the operator (intermodal goods transport) response

This paper is about distance and time in alternative bundling networks and roundtrip models. First the relevance of transport costs and time for customers of intermodal transport is reviewed. Then the paper focuses on vehicle roundtrip design in European intermodal rail networks and the perspectives to accelerate roundtrip speed. Acceleration often implies an increase of service frequency. As transport volumes often will not justify higher frequencies, the introduction of so-called complex bundling (e.g. hub-and-spoke or line services) may be an outcome. Complex bundling allows applying a relative large vehicle scale, despite of restricted flow sizes. This cost advantage is likely to overrule the cost disadvantage of longer routes in complex bundling networks. An important indication for this fact is a comparison of total network distances and times. The last part of the paper compares the distances and times of about 150 networks (different bundling concepts and network geometries). It shows that the additional length of routes of complex bundling networks is always overruled by the distance and time impact of a lower number of connections between begin- and end terminals in complex bundling network

The COOH terminus of the c-Abl tyrosine kinase contains distinct F- and G-actin binding domains with bundling activity

The myristoylated form of c-Abl protein, as well as the P210bcr/abl protein, have been shown by indirect immunofluorescence to associate with F-actin stress fibers in fibroblasts. Analysis of deletion mutants of c-Abl stably expressed in fibroblasts maps the domain responsible for this interaction to the extreme COOH-terminus of Abl. This domain mediates the association of a heterologous protein with F-actin filaments after microinjection into NIH 3T3 cells, and directly binds to F-actin in a cosedimentation assay. Microinjection and cosedimentation assays localize the actin-binding domain to a 58 amino acid region, including a charged motif at the extreme COOH-terminus that is important for efficient binding. F-actin binding by Abl is calcium independent, and Abl competes with gelsolin for binding to F- actin. In addition to the F-actin binding domain, the COOH-terminus of Abl contains a proline-rich region that mediates binding and sequestration of G-actin, and the Abl F- and G-actin binding domains cooperate to bundle F-actin filaments in vitro. The COOH terminus of Abl thus confers several novel localizing functions upon the protein, including actin binding, nuclear localization, and DNA binding. Abl may modify and receive signals from the F-actin cytoskeleton in vivo, and is an ideal candidate to mediate signal transduction from the cell surface and cytoskeleton to the nucleus

Alternative transport network designs and their implications for intermodal transhipment technologies

Six principles for operation of the rail part of intermodal rail freight transport systems are described: direct link, corridor, hub-and-spoke, connected hubs, static routes, and dynamic routes. The first part is a theoretical discussion of the characteristics of the transport network designs. The theory is then applied to intermodal freight transport by analysing how each transport network design affects the need for terminal performance. The discussion includes a classification of existing transfer technologies and an analysis of how well developed technologies meet the demands. It is concluded that there is a sufficient supply of technologies, but some need to be taken further than the current blueprint phase and prove their viability in technical and economic terms

Transmission Expansion Planning : computational challenges toward real-size networks

The importance of the transmission network for supplying electricity demand is undeniable, and Transmission Expansion Planning (TEP) studies is key for a reliable power system. Due to increasing sources of uncertainty such as more intermittent energy resources, mobile and controllable demands, and fast technology improvements for PVs and energy storage devices, the need for using systematic ways for solving this complex problem is increased. One of the main barriers for deploying optimization-based TEP studies is computationally intractability, which is the main motivation for this research. The aim of this work is to investigate the computational challenges associated with systematic TEP studies for large-scale problems, and develop algorithms to improve computational performance. In the first step, we investigate the impact of adding security constraints (as NERC standard requirement) into TEP optimization problem, and develop the Variable Contingency List (VCL) algorithm to pre-screen security constraints to only add those that may affect the feasible region. It significantly decreases the size of the problem compared to considering all security constraints. Then, we evaluate the impact of the size of candidate lines list (number of binary variables) on TEP, and developed a heuristic algorithm to decrease the size of this list. In the next step, we integrate uncertainties into the TEP optimization problem and formulate the problem as a two-stage stochastic program. Adding uncertainties increases the size of the problem significantly. It leads us to develop a three-level filter that introduces important scenario identification index (ISII) and similar scenario elimination (SSE) technique to decrease the number of security constraints in stochastic TEP in a systematic and tractable way. We then investigate the scalability of the stochastic TEP formulation. We develop a configurable decomposition framework that allows us to decompose the original problem into subproblems that can be solved independently and in parallel. This framework can benefit from using both progressive hedging (PH) and Benders decomposition (BD) algorithms to decompose and parallelize a large-scale problem both vertically and horizontally. We have also developed a bundling algorithm that improves the performance of PH algorithm and the overall performance of the framework. We have implemented our work on a reduced ERCOT network with more than 3000 buses to demonstrate the practicality of the proposed method in this work for large-scale problems.Electrical and Computer Engineerin

A Survey of the Economic Role of Software Platforms in Computer-Based Industries

Software platforms are a critical component of the computer systems underpinning leading– edge products ranging from third– generation mobile phones to video games. After describing some key economic features of computer systems and software platforms, the paper presents case studies of personal computers, video games, personal digital assistants, smart mobile phones, and digital content devices. It then compares several economic aspects of these businesses including their industry evolution, pricing structures, and degrees of integration.software platforms, hardware platforms, network effects, bundling, multi-sided markets