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Titlebook: Embedded Computer Systems: Architectures, Modeling, and Simulation; 7th International Wo Stamatis Vassiliadis,Mladen Berekovi?,Timo D. H?m?

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51#
發(fā)表于 2025-3-30 10:53:44 | 只看該作者
52#
發(fā)表于 2025-3-30 14:47:15 | 只看該作者
FPGA Design Methodology for a Wavelet-Based Scalable Video Decoderhis scalability has a lot of advantages but the corresponding decoding algorithm is complex and really stresses the system bandwidth as it replaces the block-based DCT-approach with frame-based wavelets. This has a tremendous impact on the hardware architecture. We present the implementation of the
53#
發(fā)表于 2025-3-30 19:51:54 | 只看該作者
54#
發(fā)表于 2025-3-31 00:40:47 | 只看該作者
55#
發(fā)表于 2025-3-31 04:47:04 | 只看該作者
56#
發(fā)表于 2025-3-31 07:48:38 | 只看該作者
Therapieverweigerung und Therapieverlangen,ts them successfully to guide code optimization. To give proper evidence of retargetability, we present results for the processors MIPS, PowerPC and SPARC. We obtained speed-ups of 1.18 to 1.23 over pre-optimized code.
57#
發(fā)表于 2025-3-31 11:12:42 | 只看該作者
58#
發(fā)表于 2025-3-31 17:17:37 | 只看該作者
Model and Validation of Block Cleaning Cost for Flash Memoryory strongly effect this block cleaning cost and present a model for the block cleaning cost based on these parameters. We validate this model using synthetic workloads on commercial Flash memory products.
59#
發(fā)表于 2025-3-31 20:15:04 | 只看該作者
Trade-Offs Between Voltage Scaling and Processor Shutdown for Low-Energy Embedded Multiprocessorshe total energy consumption by up to 25% for tight deadlines and by up to 57% for loose deadlines compared to DVS. We also compare the energy consumed by our scheduling algorithm to two lower bounds, and show that our best approach leaves little room for improvement.
60#
發(fā)表于 2025-3-31 23:27:02 | 只看該作者
An Automatically-Retargetable Time-Constraint-Driven Instruction Scheduler for Post-compiling Optimits them successfully to guide code optimization. To give proper evidence of retargetability, we present results for the processors MIPS, PowerPC and SPARC. We obtained speed-ups of 1.18 to 1.23 over pre-optimized code.
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