The study of reverberation, broadly defined as the multipath propagation of sound in an enclosed ... more The study of reverberation, broadly defined as the multipath propagation of sound in an enclosed space, has attracted significant interest from speech processing engineers and researchers alike. This is due in large part to the proliferation of speech processing technologies that facilitate distant talking speech input. In such scenarios, understanding the impact of reverberation on speech quality, and mitigating its detrimental impact through dereverberation techniques, are important and practically well motivated tasks. This research concerns both these topics. More specifically, in this work we (1) extend and develop an objective measure for predicting the level of perceived reverberation, (2) conduct an experimental investigation into reverberation perception and (3) propose the use of a spherical microphone array rake receiver to perform speech dereverberation. In order to assess the level of perceived reverberation in speech, we develop the extended Reverberation Decay Tail (R...
We describe the new Quartus backend of hls4ml, designed to deploy Neural Networks on Intel FPGAs.... more We describe the new Quartus backend of hls4ml, designed to deploy Neural Networks on Intel FPGAs. We list the supported network components and layer architectures (dense, binary/ternary, and convolutional neural networks) and evaluate its performance on a benchmark problem previously considered to develop the Vivado backend of hls4ml. We also introduce the support for recurrent layers and introduce a new asynchronous calling model to increase performance for larger models. In addition to that, we also demonstrate the use of this new model to optimize large-sparse networks.
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devic... more Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drastically improve experimental design and accelerate scientific discoveries. We have developed hls4ml, an open-source software-hardware co-design workflow to interpret and translate machine learning algorithms for implementation in FPGAs and ASICs specifically to support domain scientists. In this paper, we describe the essential features of the hls4ml workflow including network optimization techniques— such as pruning and quantization-aware training—which can be incorporated naturally into the device implementations. We expand on previous hls4ml work by extending capabilities and techniques towards low-power implementations and increased usability: new PythonAPIs, quantization-aware pruning, end-to-end FPGAworkflows, long pipeline kernels for l...
The study of reverberation, broadly defined as the multipath propagation of sound in an enclosed ... more The study of reverberation, broadly defined as the multipath propagation of sound in an enclosed space, has attracted significant interest from speech processing engineers and researchers alike. This is due in large part to the proliferation of speech processing technologies that facilitate distant talking speech input. In such scenarios, understanding the impact of reverberation on speech quality, and mitigating its detrimental impact through dereverberation techniques, are important and practically well motivated tasks. This research concerns both these topics. More specifically, in this work we (1) extend and develop an objective measure for predicting the level of perceived reverberation, (2) conduct an experimental investigation into reverberation perception and (3) propose the use of a spherical microphone array rake receiver to perform speech dereverberation. In order to assess the level of perceived reverberation in speech, we develop the extended Reverberation Decay Tail (R...
We describe the new Quartus backend of hls4ml, designed to deploy Neural Networks on Intel FPGAs.... more We describe the new Quartus backend of hls4ml, designed to deploy Neural Networks on Intel FPGAs. We list the supported network components and layer architectures (dense, binary/ternary, and convolutional neural networks) and evaluate its performance on a benchmark problem previously considered to develop the Vivado backend of hls4ml. We also introduce the support for recurrent layers and introduce a new asynchronous calling model to increase performance for larger models. In addition to that, we also demonstrate the use of this new model to optimize large-sparse networks.
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devic... more Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drastically improve experimental design and accelerate scientific discoveries. We have developed hls4ml, an open-source software-hardware co-design workflow to interpret and translate machine learning algorithms for implementation in FPGAs and ASICs specifically to support domain scientists. In this paper, we describe the essential features of the hls4ml workflow including network optimization techniques— such as pruning and quantization-aware training—which can be incorporated naturally into the device implementations. We expand on previous hls4ml work by extending capabilities and techniques towards low-power implementations and increased usability: new PythonAPIs, quantization-aware pruning, end-to-end FPGAworkflows, long pipeline kernels for l...
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Papers by Hamza Javed