Sensor networks is among the fastest growing technologies that have the potential of changing our lives drastically. These collaborative, dynamic and distributed computing and communicating systems will be self organizing. They will have capabilities of distributing a task among themselves for efficient computation. There are many challenges in implementation of such systems: energy dissipation and clustering being one of them. In order to maintain a certain degree of service quality and a reasonable system lifetime, energy needs to be optimized at every stage of system operation. Sensor node clustering is another very important optimization problem. Nodes that are clustered together will easily be able to communicate with each other. Considering energy as an optimization parameter while clustering is imperative. In this paper we study the theoretical aspects of the clustering problem in sensor networks with application to energy optimization. We illustrate an optimal algorithm for clustering the sensor nodes such that each cluster (which has a master) is balanced and the total distance between sensor nodes and master nodes is minimized. Balancing the clusters is needed for evenly distributing the load on all master nodes. Minimizing the total distance helps in reducing the communication overhead and hence the energy dissipation. This problem (which we call balanced k-clustering) is modeled as a mincost flow problem which can be solved optimally using existing techniques.
Convolutional neural networks (CNNs) have led to remarkable progress in a number of key pattern recognition tasks, such as visual scene understanding and speech recognition, that potentially enable numerous applications. Consequently, there is a significant need to deploy trained CNNs to resource-constrained embedded systems. Inference using pretrained modern deep CNNs, however, requires significant system resources, including computation, energy, and memory space. To enable efficient implementation of trained CNNs, a viable approach is to approximate the network with an implementation-friendly model with only negligible degradation in classification accuracy. We present Ristretto, a CNN approximation framework that enables empirical investigation of the tradeoff between various number representation and word width choices and the classification accuracy of the model. Specifically, Ristretto analyzes a given CNN with respect to numerical range required to represent weights, activations, and intermediate results of convolutional and fully connected layers, and subsequently, it simulates the impact of reduced word width or lower precision arithmetic operators on the model accuracy. Moreover, Ristretto can fine-tune a quantized network to further improve its classification accuracy under a given number representation and word width configuration. Given a maximum classification accuracy degradation tolerance of 1%, we use Ristretto to demonstrate that three ImageNet networks can be condensed to use 8-bit dynamic fixed point for network weights and activations. Ristretto is available as a popular open-source software project and has already been viewed over 1,000 times on Github as of the submission of this brief.
Reconfiguration delay is one of the major barriers in the way of dynamically adapting a system to its application requirements. The run-time reconfiguration delay is quite comparable to the application latency for many classes of applications and might even dominate the application run-time. In this paper, we present an efficient optimal algorithm for minimizing the run-time reconfiguration (context switching) delay of executing an application on a dynamically adaptable system. The system is composed of a number of cameras with embedded reconfigurable resources collaborating in order to track an object. The operations required to execute in order to track the object are revealed to the system at run-time and can change according to a number of parameters, such as the target shape and proximity. Similarly, we can assume that the applications comprising tasks are already scheduled and each of them has to be realized on the reconfigurable fabric in order to be executed.The modeling and the algorithm are both applicable to partially reconfigurable platforms as well as multi-FPGA systems. The algorithm can be directly applied to minimize the application runtime for the typical classes of applications, where the actual execution delay of the basic operations is negligible compared to the reconfiguration delay. We prove the optimality and the efficiency of our algorithm. We report the experimental results, which demonstrate a 2.5-40% improvement on the total run-time reconfiguration delay as compared to other heuristics.
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