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neural networks (NNs) for multiple hardware impairments mitigation of a realistic direct conversion transmitter are impractical due to high computational complexity. We propose two methods to reduce the complexity without significant performance penalty. First, propose a novel NN with shortcut connections, referred to as shortcut real-valued time-delay neural network (SVDEN), where trainable neuron-wise shortcut connections are added between the input and output layers. Second, we implement a NN pruning algorithm that gradually removes connections corresponding to minimal weight magnitudes in each layer. Simulation and experimental results show that SVDEN with pruning achieves better performance for compensating frequency-dependent quadrature imbalance and power amplifier nonlinearity than other NN-based and Volterra-based models, while requiring less or similar complexity. [ABSTRACT FROM AUTHOR]

Deadlock is a critical issue in faulty Networks-on-Chips (NoCs). Existing deadlock-free approaches on faulty NoCs suffer from low throughput and poor fairness when the network becomes oversaturated. This problem hinders their practical use as oversaturation scenarios are more frequent on faulty NoCs. To address this issue, a deflection-based deadlock recovery framework is proposed for higher oversaturation performance on faulty NoCs. First, we observe the low oversaturation performance of existing deadlock recovery approaches, and analyze the positive feedback loop that can amplify the negative impact of deadlocks and congestions, which necessitate handling both deadlocks and congestions in a deadlock recovery framework. Second, we propose a novel deadlock recovery framework, which includes an accurate, timely deadlock detection and a highly efficient deadlock recovery. Both the deadlock detection and recovery reduce the average packet traversal latency, thereby improving the average oversaturation throughput. Third, we propose a distributed implementation to make the entire network enter and exit the deflection mode, which is conducted by broadcasting special messages via a bufferless subnetwork. An average oversaturation throughput improvement of $1.1\sim 8.1\times $ over state-of-the-art approaches is achieved. In terms of fairness, the minimal oversaturation throughput is improved from near zero to half of the peak throughput. [ABSTRACT FROM AUTHOR]

Power gating is effective for networks-on-chip (NoCs) to reduce the excessive leakage power dissipated by idle network components. Most existing NoC power-gating approaches rely on the routing algorithms to mitigate the power-gating blocking latency problem. When the network becomes faulty and fault-tolerant routing algorithms are applied, these approaches are no longer applicable or can seriously degrade the performance. Other approaches propose fine-grained buffer power gating, but they are too conservative in power saving due to the buffer backpressure flow control. To address these problems, we propose an aggressive fine-grained power gating of flit-sized buffer entries by adopting backpressureless flow control in an input-buffered network. The power-gating decisions are made based on the flit deflection rate. However, directly applying the backpressureless flow control leads to the difficulties of multiflit packet truncation and protocol deadlocks. Therefore, we modify the packet injection architecture to avoid packet truncation. This is done by chaining the local input port with a randomly chosen input port. Finally, we design a progressive recovery framework to handle both livelocks and protocol deadlocks. It does not need to truncate packets or strictly separate different message classes when the network is free of livelocks or protocol deadlocks. The experimental results show that with a hardware overhead of 9.6%, our design can save up to 59% network power consumption in both a fault-free and a faulty NoC with little zero-load latency penalty. Our design also approaches an ideal energy-proportional NoC because it can constantly reduce power consumption over a wide range of injection rates. [ABSTRACT FROM AUTHOR]

Photos obtained via crowdsourcing can be used in many critical applications. Due to the limitations of communication bandwidth, storage, and processing capability, it is a challenge to transfer the huge amount of crowdsourced photos. To address this problem, we propose a framework, called SmartPhoto, to quantify the quality (utility) of crowdsourced photos based on the accessible geographical and geometrical information (called metadata ) including the smartphone's orientation, position, and all related parameters of the built-in camera. From the metadata, we can infer where and how the photo is taken, and then only transmit the most useful photos. Four optimization problems regarding the tradeoffs between photo utility and resource constraints, namely Max-Utility, online Max-Utility, Min-Selection, and Min-Selection with $k$ -coverage, are studied. Efficient algorithms are proposed and their performance bounds are theoretically proved. We have implemented SmartPhoto in a testbed using Android based smartphones, and proposed techniques to improve the accuracy of the collected metadata by reducing sensor reading errors and solving object occlusion issues. Results based on real implementations and extensive simulations demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]