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Several applications in machine learning and machine-to-human interactions tolerate small deviations in their computations. Digital systems can exploit this fault-tolerance to increase their energy-efficiency, which is crucial in embedded applications. Hence, this paper introduces a new means of Approximate Computing: Dynamic-Voltage-Accuracy-Frequency-Scaling (DVAFS), a circuit-level technique enabling...
ConvNets, or Convolutional Neural Networks (CNN), are state-of-the-art classification algorithms, achieving near-human performance in visual recognition [1]. New trends such as augmented reality demand always-on visual processing in wearable devices. Yet, advanced ConvNets achieving high recognition rates are too expensive in terms of energy as they require substantial data movement and billions of...
In-band full duplex (IBFD) is a promising technique that allows to potentially double the achievable bi-directional throughput over a given bandwidth. Moreover, it has been estimated that IBFD-equipped wireless networks can be more energy-efficient than half duplex ones due to the reduced energy cost of packet collisions. However, one key challenge for implementing an energy-efficient IBFD system...
In this work, an FFT architecture supporting variable FFT sizes, 128∼2048/1536, is proposed. This implementation is a combination of a 2p point Common Factor FFT and a 3 point DFT. Various FFT output pruning techniques for this architecture are discussed in terms of memory and control logic overhead. It is shown that the used Prime Factor FFT as an FFT in the 1536 point FFT is able to increase throughput...
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