Michael Deisher

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Recent advances in computing have led to an explosion in the amount of data being generated. Processing the ever-growing data in a timely manner has made throughput computing an important aspect for emerging applications. Our analysis of a set of important throughput computing kernels shows that there is an ample amount of parallelism in these kernels which(More)
The design of an activity recognition and monitoring system based on the eWatch, multi-sensor platform worn on different body positions, is presented in this paper. The system identifies the user's activity in realtime using multiple sensors and records the classification results during a day. We compare multiple time domain feature sets and sampling rates,(More)
Context-aware mobile computing requires wearable sensors to acquire information about the user. Continuous sensing rapidly depletes the wearable system’s energy, which is a critically constrained resource. In this paper, we analyze the trade-off between power consumption and prediction accuracy of context classifiers working on dual-axis accelerometer data(More)
An audio front-end with Voice Activity Detection (VAD) hardware targeted for low-power embedded SoCs, featuring a 512pt FFT, programmable filters, noise floor estimator and a decision engine has been fabricated in 32nm CMOS. The dual-V<sub>CC</sub>, dual-frequency design allows the core datapath to scale to near-threshold voltage, where power consumption is(More)
Designing high-performance LU factorization for modern hybrid multi/many-core systems requires highly-tuned BLAS subroutines, hiding communication latency and balancing the load across devices of variable processing capabilities. In this paper we show how single-precision LU factorization is accelerated on Intel® MIC(Many Integrated Core) architecture in(More)
We analyze the use of selective sampling strategies to aid in power conservation in sensor platforms for context-aware systems. In particular, we study an activity-aware system based on the eWatch sensor and notification platform, developed at CMU. We collected 94 hours of self-annotated activity data from four subjects over several days each. We compare(More)
A new method for reduction of computation and memory bandwidth for embedded large vocabulary continuous speech recognition is presented. During the Hidden Markov model state likelihood computation, scores for selected context-dependent (triphone) model states are computed for several frames in advance. Scores that are subsequently needed for Viterbi search(More)
In recent years many signal processing applications involving classification, detection, and inference have enjoyed substantial accuracy improvements due to advances in deep learning. At the same time, the “Internet of Things” has become an important class of devices. Although the paradigm of local sensing and remote inference has been very successful(More)
A smart home controller that responds to natural language input is demonstrated on an Intel® embedded processor. This device contains two DSP cores and a neural network co-processor which share 4MB SRAM. An embedded configuration of the Intel® RealSpeechTM speech recognizer and intent extraction engine runs on the DSP cores with neural network operations(More)