11,679 research outputs found
Low Power system Design techniques for mobile computers
Portable products are being used increasingly. Because these systems are battery powered, reducing power consumption is vital. In this report we give the properties of low power design and techniques to exploit them on the architecture of the system. We focus on: min imizing capacitance, avoiding unnecessary and wasteful activity, and reducing voltage and frequency. We review energy reduction techniques in the architecture and design of a hand-held computer and the wireless communication system, including error control, sys tem decomposition, communication and MAC protocols, and low power short range net works
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
Design techniques for low-power systems
Portable products are being used increasingly. Because these systems are battery powered, reducing power consumption is vital. In this report we give the properties of low-power design and techniques to exploit them on the architecture of the system. We focus on: minimizing capacitance, avoiding unnecessary and wasteful activity, and reducing voltage and frequency. We review energy reduction techniques in the architecture and design of a hand-held computer and the wireless communication system including error control, system decomposition, communication and MAC protocols, and low-power short range networks
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
We present PyCARL, a PyNN-based common Python programming interface for
hardware-software co-simulation of spiking neural network (SNN). Through
PyCARL, we make the following two key contributions. First, we provide an
interface of PyNN to CARLsim, a computationally-efficient, GPU-accelerated and
biophysically-detailed SNN simulator. PyCARL facilitates joint development of
machine learning models and code sharing between CARLsim and PyNN users,
promoting an integrated and larger neuromorphic community. Second, we integrate
cycle-accurate models of state-of-the-art neuromorphic hardware such as
TrueNorth, Loihi, and DynapSE in PyCARL, to accurately model hardware latencies
that delay spikes between communicating neurons and degrade performance. PyCARL
allows users to analyze and optimize the performance difference between
software-only simulation and hardware-software co-simulation of their machine
learning models. We show that system designers can also use PyCARL to perform
design-space exploration early in the product development stage, facilitating
faster time-to-deployment of neuromorphic products. We evaluate the memory
usage and simulation time of PyCARL using functionality tests, synthetic SNNs,
and realistic applications. Our results demonstrate that for large SNNs, PyCARL
does not lead to any significant overhead compared to CARLsim. We also use
PyCARL to analyze these SNNs for a state-of-the-art neuromorphic hardware and
demonstrate a significant performance deviation from software-only simulations.
PyCARL allows to evaluate and minimize such differences early during model
development.Comment: 10 pages, 25 figures. Accepted for publication at International Joint
Conference on Neural Networks (IJCNN) 202
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