MISAT: Designing a Series of Powerful Small Satellites Based upon Micro Systems Technology

MISAT is a research and development cluster which will create a small satellite platform based on Micro Systems Technology (MST) aiming at innovative space as well as terrestrial applications. MISAT is part of the Dutch MicroNed program which has established a microsystems infrastructure to fully exploit the MST knowledge chain involving public and industrial partners alike. The cluster covers MST-related developments for the spacecraft bus and payload, as well as the satellite architecture. Particular emphasis is given to distributed systems in space to fully exploit the potential of miniaturization for future mission concepts. Examples of current developments are wireless sensor and actuator networks with plug and play characteristics, autonomous digital Sun sensors, re-configurable radio front ends with minimum power consumption, or micro-machined electrostatic accelerometer and gradiometer system for scientific research in fundamental physics as well as geophysics. As a result of MISAT, a first nano-satellite will be launched in 2007 to demonstrate the next generation of Sun sensors, power subsystems and satellite architecture technology. Rapid access to in-orbit technology demonstration and verification will be provided by a series of small satellites. This will include a formation flying mission, which will increasingly rely on MISAT technology to improve functionality and reduce size, mass and power for advanced technology demonstration and novel scientific applications.

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