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We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech–two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks , end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different(More)
BACKGROUND It has been well established that arterial stiffness, manifest as an increase in arterial pulse wave velocity or late systolic amplification of the carotid artery pressure pulse, increases with age. However, the populations studied in prior investigations were not rigorously screened to exclude clinical hypertension, occult coronary disease, or(More)
We demonstrate a dynamic Verilog-A RRAM compact model capable of simulating real-time DC cycling and pulsed operation device behavior, including random variability that is inherent to RRAM. This paper illustrates the physics and capabilities of the model. The model is verified using different sets of experimental data. The DC/Pulse parameter fitting(More)
This paper introduces a new technique for mapping Deep Recurrent Neural Networks (RNN) efficiently onto GPUs. We show how it is possible to achieve substantially higher computational throughput at low mini-batch sizes than direct implementations of RNNs based on matrix multiplications. The key to our approach is the use of persistent computational kernels(More)
Deep learning has dramatically improved the performance of speech recognition systems through learning hierarchies of features optimized for the task at hand. However, true end-to-end learning, where features are learned directly from wave-forms, has only recently reached the performance of hand-tailored representations based on the Fourier transform. In(More)
Generative models in vision have seen rapid progress due to algorithmic improvements and the availability of high-quality image datasets. In this paper, we offer contributions in both these areas to enable similar progress in audio modeling. First, we detail a powerful new WaveNet-style autoen-coder model that conditions an autoregres-sive decoder on(More)
The exponential growth in data generation and large-scale data analysis creates an unprecedented need for inexpensive, low-latency, and high-density information storage. This need has motivated significant research into multi-level memory systems that can store multiple bits of information per device. Although both the memory state of these devices and much(More)
Traditional approaches to memory characterize the number of distinct states achievable at a given Raw Bit Error Rate (RBER). Using Phase Change Memory (PCM) as an example analog-valued memory, we demonstrate that measuring the mutual information allows optimal design of read-write circuits to increase data storage capacity by 30%. Further, we show the(More)
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