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Field-programmable gate arrays (FPGAs) can provide an efficient programmable resource for implementing hardware-based spiking neural networks (SNN). In this paper we present a hardware-software design that makes it possible to simulate large-scale (2 million neurons) biologically plausible SNNs on an FPGA-based system. We have chosen three SNN models from the various models available in the literature,...
Modeling the execution of a processor and its instructions is a challenging problem, in particular in the presence of long pipelines, parallelism, and out-of-order execution. A naive approach based on finite state automata inevitably leads to an explosion in the number of states and is thus only applicable to simple minimalistic processors. During their execution, instructions may only proceed forward...
This paper presents a parallel algorithm for computing one dimensional unstable manifold of a hyperbolic fixed point of discrete dynamical system. It is pointed out that parallel computing can be realized by subdividing the unstable manifold into mutually independent subsections. In each subsection, the one dimensional unstable manifold is grown by forward iteration. Curvature constraint and distance...
The generalized multi frame (GMF) task has been proposed to model a task whose execution times, deadlines and minimum separation times are changed according to a specified pattern. In this paper we relax the assumption of having a specified activation pattern, this yields to non-cyclic GMF task. In this context, current schedulability analysis techniques for GMF task sets under dynamic priority assignment...
Many-task computing (MTC) is a practical paradigm for developing loosely coupled and complex scientific applications. In this paradigm, computation on a large dataset is decomposed into tasks that are expected to be executed in parallel with dynamically allocated computing resources. These tasks pass data via files, and each one is to execute an existing program on one dataset element. Task scheduling...
Tasks running on embedded systems are often associated with deadlines. While it is important to complete tasks before their associated deadlines, performance and energy consumption also play important roles in many usages of embedded systems. To address these issues, we explore the use of Dynamic Voltage and Frequency Scaling (DVFS), a standard feature available on many modern processors for embedded...
Mathematical models of many real life optimization problems turn out to be so complex that traditional optimization techniques such as gradient based methods and other deterministic techniques etc are not applicable for them. A new class of optimization techniques called population-based meta-heuristics (PBM) is applied for the solution of these problems. Particle Swarm Optimization algorithm (PSO)...
In domains of VLSI and the rising SoC, the system design exceedingly depends on the simulation and modeling. Conventional HDLs have some weakness including the extravagant precision and the slow speed. On the other hand, the system-level modeling, such as SystemC, has been widely used on all kinds of projects and achieved favorable results. When the target system scales excessively up, the simulation...
We propose using a profiling-based technique (dynamic critical path) to guide SoC optimization. Optimizing SoCs composed of many modules involves exploring a large space of possible configurations (exponential in the number of component modules). We present this optimization technique applied to a globally asynchronous locally synchronous (GALS) RTL design. Furthermore, we investigate the loss of...
Multicore processors promise continued hardware performance improvements even as single-core performance flattens out. However they also enable increasingly complex application software that threatens to obfuscate application-level performance. This paper applies operational analysis to the problem of understanding and predicting application-level performance in parallel servers. We present operational...
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