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Machine Learning techniques such as Support Vector Machines (SVM) have found applications in many fields, e.g. in Wireless Sensor Networks (WSN) and sensor data processing in general. Especially in the case of WSN energy is very limited as agents solely operate based on battery power after they have been deployed, therefore energy efficiency is of great importance. Furthermore, agents are supposed...
Processor models for electronic system level (ESL) simulations are usually provided by their vendors as binary object code. Those binaries appear as black boxes, which do not allow to observe their internals. This prevents the application of most existing ESL power estimation methodologies. To remedy this situation, this work presents an estimation methodology for the case of black box models. The...
The problem of finding an optimal allocation of logical data buffers to memory has emerged as a new research challenge due to the increased complexity of applications and new emerging Dynamic RAM (DRAM) interface technologies. This new opportunity of a large off-chip memory accessible by an ample bandwidth allows to reduce the on-chip Static RAM (SRAM) significantly and save production cost of future...
In the past memory allocation and communication between processors and memories in current MPSoC's, due to the small design space, was not a big challenge. Through advanced MPSoC's and improving techniques to interface Dynamic RAM (DRAM), allocation of logical data buffers to physical memories is no longer manageable manually. We present a heuristic for the mapping of logical data buffers to physical...
With the availability of advanced MPSoC and emerging Dynamic RAM (DRAM) interface technologies, an optimal allocation of logical data buffers to physical memory cannot be handled manually anymore due to the huge design space. An allocation does not only need to decide between an on-or off-chip memory, but also needs to take an increasing number of available memory channels, different bandwidth capacities...
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