Deep neural networks (DNNs) have gained considerable attention in various real-world applications due to the strong performance on representation learning. However, a DNN needs to be trained many epochs for pursuing a higher inference accuracy, which requires storing sequential versions of DNNs and releasing the updated versions to users. As a result, large amounts of storage and network resources are required, which significantly hamper DNN utilization on resource-constrained platforms (e.g., IoT, mobile phone). In this paper, we present a novel delta compression framework called Delta-DNN, which can efficiently compress the float-point numbers in DNNs by exploiting the floats similarity existing in DNNs during training. Specifically, (1) we observe the high similarity of float-point numbers between the neighboring versions of a neural network in training; (2) inspired by delta compression technique, we only record the delta (i.e., the differences) between two neighboring versions, instead of storing the full new version for DNNs; (3) we use the error-bounded lossy compression to compress the delta data for a high compression ratio, where the error bound is strictly assessed by an acceptable loss of DNNs’ inference accuracy; (4) we evaluate Delta-DNN’s performance on two scenarios, including reducing the transmission of releasing DNNs over network and saving the storage space occupied by multiple versions of DNNs. According to experimental results on six popular DNNs, DeltaDNN achieves the compression ratio 2x~10x higher than state-ofthe-art methods, while without sacrificing inference accuracy and changing the neural network structure.
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This content will become publicly available on June 4, 2024
ERSAM: Neural Architecture Search for Energy-Efficient and Real-Time Social Ambiance Measurement
Social ambiance describes the context in which social interactions happen, and can be measured using speech audio by counting the number of concurrent speakers. This measurement has enabled various mental health tracking and human-centric IoT applications. While on-device Socal Ambiance Measure (SAM) is highly desirable to ensure user privacy and thus facilitate wide adoption of the aforementioned applications, the required computational complexity of state-of-the-art deep neural networks (DNNs) powered SAM solutions stands at odds with the often constrained resources on mobile devices. Furthermore, only limited labeled data is available or practical when it comes to SAM under clinical settings due to various privacy constraints and the required human effort, further challenging the achievable accuracy of on-device SAM solutions. To this end, we propose a dedicated neural architecture search framework for Energy-efficient and Real-time SAM (ERSAM). Specifically, our ERSAM framework can automatically search for DNNs that push forward the achievable accuracy vs. hardware efficiency frontier of mobile SAM solutions. For example, ERSAM-delivered DNNs only consume 40 mW • 12 h energy and 0.05 seconds processing latency for a 5 seconds audio segment on a Pixel 3 phone, while only achieving an error rate of 14.3% on a social ambiance dataset generated by LibriSpeech. We can expect that our ERSAM framework can pave the way for ubiquitous on-device SAM solutions which are in growing demand.
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- Award ID(s):
- 1937592
- NSF-PAR ID:
- 10487907
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Location:
- Rhodes Island, Greece
- Sponsoring Org:
- National Science Foundation
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