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In order to fulfill the ever-increasing demand for high-speed and high-bandwidth, wireless-based MCSoC is presented based on a NoC communication infrastructure. Inspiring the separation between the communication and the computation demands as well as providing the flexible topology configurations, makes wireless-based NoC a promising future MCSoC architecture. However, congestion occurrence in wireless routers reduces the benefit of high-speed wireless links and significantly increases the network latency. Therefore, in this paper, a congestion-aware platform, named CAP-W, is introduced for wireless-based NoC in order to reduce congestion in the network and especially over wireless routers. The triple-layer platform of CAP-W is composed of mapping, migration, and routing layers. In order to minimize the congestion probability, the mapping layer is responsible for selecting the suitable free core as the first candidate, finding the suitable first task to be mapped onto the selected core, and allocating other tasks with respect to contiguity. Considering dynamic variation of application behaviors, the migration layer modifies the primary task mapping to improve congestion situation. Furthermore, the routing layer balances utilization of wired and wireless networks by separating short-distance and long-distance communications. Experimental results show meaningful gain in congestion control of wireless-based NoC compared to state-of-the-art works. [ABSTRACT FROM AUTHOR] more...

Coarse Grained Reconfigurable Architectures (CGRAs) are emerging as enabling platforms to meet the high performance demanded by modern applications (e.g. 4G, CDMA, etc.). Recently proposed CGRAs offer time-multiplexing and dynamic applications parallelism to enhance device utilization and reduce energy consumption at the cost of additional memory (up to 50% area of the overall platform). To reduce the memory overheads, novel CGRAs employ either statistical compression, intermediate compact representation, or multicasting. Each compaction technique has different properties (i.e. compression ratio, decompression time and decompression energy) and is best suited for a particular class of applications. However, existing research only deals with these methods separately. Moreover, they only analyze the compaction ratio and do not evaluate the associated energy overheads. To tackle these issues, we propose a polymorphic compression architecture that interleaves these techniques in a unique platform. The proposed architecture allows each application to take advantage of a separate compression/decompression hierarchy (consisting of various types and implementations of hardware/software decoders) tailored to its needs. Simulation results, using different applications (FFT, Matrix multiplication, and WLAN), reveal that the choice of compression hierarchy has a significant impact on compression ratio (up to 52%), decompression energy (up to 4 orders of magnitude), and configuration time (from 33 n to 1.5 s) for the tested applications. Synthesis results reveal that introducing adaptivity incurs negligible additional overheads (1%) compared to the overall platform area. [ABSTRACT FROM AUTHOR] more...

This paper presents a comprehensive software-based technique that is capable of detecting soft errors in embedded systems. Soft errors can be categorized into Control Flow Errors (CFEs) and data errors. The CFEs change the flow of the program erroneously and data errors also change the results. In this paper, a new comprehensive method is presented to detect both (based on combination of authors' previous works). In order to evaluate the proposed method, a new factor is defined that considers three main parameters simultaneously; namely fault coverage, memory overhead, and performance overhead. Since these parameters are very important in safety critical applications, they should be improved concurrently. The experimental results on SPEC2000 benchmarks show that the Evaluation Factor of the proposed method is 50% better than the Relationship Signatures for Control Flow Checking with Data Validation (RSCFCDV) methods, which are suggested in the literature. [ABSTRACT FROM AUTHOR] more...

Due to the ever-escalating power consumption, a significant proportion of the future many-core chips is mandatory to be switched off to meet the power budgets. This trend has brought up a paradigm shift from conventional low-power to power budgeting designs, where performance optimization needs to be performed under a tight power budget constraint. There are two key issues to be considered when moving this new design paradigm forward. Firstly, with per-core frequency scaling, the number of frequency combinations of the cores grows exponentially. As more cores are integrated onto a chip, it becomes more challenging to achieve the optimal performance over a given power budget. Secondly, the power budgets of many-core system might undergo a rapid fluctuation. Consequently, the power budgeting scheme needs to be prompt to make appropriate changes to track such power budget variation. This paper is aiming at resolving the problem of optimizing overall performance over a power budget using frequency scaling technique. To solve the problem efficiently at runtime, we propose a parallel dynamic programming network, in which the Pareto-optimal solutions can be obtained using linear time complexity. Experimental results have confirmed that the proposed approach can reduce the execution time by 45% when compared to other existing methods. The runtime overhead and hardware cost of the proposed approach are reasonably small, such as the average area and power consumption are less than 1% of the whole network-on-chip. This paper demonstrates an effective formulation for delivering Pareto-optimal solutions for power budgeting in future many-core systems. [ABSTRACT FROM AUTHOR] more...

Deep Neural Networks (DNNs) are compute-intensive learning models with growing applicability in a wide range of domains. Due to their computational complexity, DNNs benefit from implementations that utilize custom hardware accelerators to meet performance and response time as well as classification accuracy constraints. In this paper, we propose DeepMaker framework that aims to automatically design a set of highly robust DNN architectures for embedded devices as the closest processing unit to the sensors. DeepMaker explores and prunes the design space to find improved neural architectures. Our proposed framework takes advantage of a multi-objective evolutionary approach that exploits a pruned design space inspired by a dense architecture. DeepMaker considers the accuracy along with the network size factor as two objectives to build a highly optimized network fitting with limited computational resource budgets while delivers an acceptable accuracy level. In comparison with the best result on the CIFAR-10 dataset, a generated network by DeepMaker presents up to a 26.4x compression rate while loses only 4% accuracy. Besides, DeepMaker maps the generated CNN on the programmable commodity devices, including ARM Processor, High-Performance CPU, GPU, and FPGA. [ABSTRACT FROM AUTHOR] more...