There has been a booming demand for integrating Convolutional Neural Networks (CNNs) powered functionalities into Internet-of-Thing (IoT) devices to enable ubiquitous intelligent "IoT cameras". However, more extensive applications of such IoT systems are still limited by two challenges. First, some applications, especially medicine-and wearable-related ones, impose stringent requirements on the camera form factor. Second, powerful CNNs often require considerable storage and energy cost, whereas IoT devices often suffer from limited resources. PhlatCam, with its form factor potentially reduced by orders of magnitude, has emerged as a promising solution to the first aforementioned challenge, while the second one remains a bottleneck. Existing compression techniques, which can potentially tackle the second challenge, are far from realizing the full potential in storage and energy reduction, because they mostly focus on the CNN algorithm itself. To this end, this work proposes SACoD, a Sensor Algorithm Co-Design framework to develop more efficient CNN-powered PhlatCam. In particular, the mask coded in the Phlat-Cam sensor and the backend CNN model are jointly optimized in terms of both model parameters and architectures via differential neural architecture search. Extensive experiments including both simulation and physical measurement on manufactured masks show that the proposed SACoD framework achieves aggressive model compressionmore »
Towards Physical Design Management in Storage Systems
In the post-Moore era, systems and devices with new architectures will arrive at a rapid rate with significant impacts on the software stack. Applications will not be able to fully benefit from new architectures unless they can delegate adapting to new devices in lower layers of the stack. In this paper we introduce physical design management which deals with the problem of identifying and executing transformations on physical designs of stored data, i.e. how data is mapped to storage abstractions like files, objects, or blocks, in order to improve performance. Physical design is traditionally placed with applications, access libraries, and databases, using hard- wired assumptions about underlying storage systems. Yet, storage systems increasingly not only contain multiple kinds of storage devices with vastly different performance profiles but also move data among those storage devices, thereby changing the benefit of a particular physical design. We advocate placing physical design management in storage, identify interesting research challenges, provide a brief description of a prototype implementation in Ceph, and discuss the results of initial experiments at scale that are replicable using Cloudlab. These experiments show performance and resource utilization trade-offs associated with choosing different physical designs and choosing to transform between physical designs.
- Award ID(s):
- 1836650
- Publication Date:
- NSF-PAR ID:
- 10166880
- Journal Name:
- 2019 IEEE/ACM Fourth International Parallel Data Systems Workshop (PDSW)
- Page Range or eLocation-ID:
- 40 to 49
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1109/PDSW49588.2019.00009
