FlexiDRAM: A Flexible in-DRAM Framework to Enable Parallel General-Purpose Computation
Document Type
Conference Proceeding
Publication Date
8-2-2022
Abstract
In this paper, we propose a Flexible processing-in-DRAM framework named FlexiDRAM that supports the efficient implementation of complex bulk bitwise operations. This framework is developed on top of a new reconfigurable in-DRAM accelerator that leverages the analog operation of DRAM sub-arrays and elevates it to implement XOR2-MAJ3 operations between operands stored in the same bit-line. FlexiDRAM first generates an efficient XOR-MAJ representation of the desired logic and then appropriately allocates DRAM rows to the operands to execute any in-DRAM computation. We develop ISA and software support required to compute in-DRAM operation. FlexiDRAM transforms current memory architecture to a massively parallel computational unit and can be leveraged to significantly reduce the latency and energy consumption of complex workloads. Our extensive circuit-to-architecture simulation results show that averaged across two well-known deep learning workloads, FlexiDRAM achieves ~15 energy-saving and 13 speedup over the GPU outperforming recent processing-in-DRAM platforms.
Identifier
85136247397 (Scopus)
ISBN
[9781450393546]
Publication Title
Proceedings of the International Symposium on Low Power Electronics and Design
External Full Text Location
https://doi.org/10.1145/3531437.3539721
ISSN
15334678
Grant
1710009
Fund Ref
International Studies Association
Recommended Citation
Zhou, Ranyang; Roohi, Arman; Misra, Durga; and Angizi, Shaahin, "FlexiDRAM: A Flexible in-DRAM Framework to Enable Parallel General-Purpose Computation" (2022). Faculty Publications. 2736.
https://digitalcommons.njit.edu/fac_pubs/2736