Jehanzeb Mirza, Leonid Karlinsky, et al.
NeurIPS 2023
We present algorithms for binary cube networks that emulate butterfly network computations on binary-reflected Gray-coded data in the same time complexity as that required for binary-coded data. The code conversion required for the emulation with binary-reflected Gray-coded data is either performed in local memories or through concurrent exchanges. The emulation of a butterfly network with one or two rows mapped to each binary cube node requires n communication cycles on an n-cube. For more than two rows per node, one additional communication cycle is required for every pair of rows, with concurrent communication on all channels of every node. The encoding upon completion can be either binary, or binary-reflected Gray code, or any combination thereof, without affecting the communication complexity. © 1994 Academic Press, Inc.
Jehanzeb Mirza, Leonid Karlinsky, et al.
NeurIPS 2023
Annina Riedhauser, Viacheslav Snigirev, et al.
CLEO 2023
Rangachari Anand, Kishan Mehrotra, et al.
IEEE Transactions on Neural Networks
Harsha Kokel, Aamod Khatiwada, et al.
VLDB 2025