Jesse Engel

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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)
—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)
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)
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