Sparse Data Acquisition on Emerging Memory Architectures
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Threat Intelligence Center
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Emerging memory devices, such as resistive crossbars, have the capacity to store large amounts of data in a single array. Acquiring the data stored in large-capacity crossbars in a sequential fashion can become a bottleneck. We present practical methods, based on sparse sampling, to quickly acquire sparse data stored on emerging memory devices that support the basic summation kernel, reducing the acquisition time from linear to sub-linear. The experimental results show that at least an order of magnitude improvement in acquisition time can be achieved when the data are sparse. Finally, in addition, we show that the energy cost associated with our approach is competitive to that of the sequential method.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1839852
- Alternate ID(s):
- OSTI ID: 1492354
- Report Number(s):
- SAND-2018-14005J; 670902
- Journal Information:
- IEEE Access, Vol. 7; ISSN 2169-3536
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
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