US 12,255,670 B2
Compression strategy selection powered by machine learning
Vladimir Shalikashvili, Petah Tikva (IL); and Ran Sandhaus, Tel Aviv (IL)
Assigned to Mellanox Technologies, Ltd, Yokneam (IL)
Filed by MELLANOX TECHNOLOGIES, LTD., Yokneam (IL)
Filed on Aug. 18, 2022, as Appl. No. 17/890,337.
Prior Publication US 2024/0063814 A1, Feb. 22, 2024
Int. Cl. H03M 7/30 (2006.01); G06F 3/06 (2006.01); G06F 9/50 (2006.01); G06F 12/16 (2006.01)
CPC H03M 7/6088 (2013.01) [G06F 3/06 (2013.01); G06F 9/50 (2013.01); G06F 12/16 (2013.01)] 28 Claims
OG exemplary drawing
 
1. A data compression system comprising:
a computer memory to store plural compression algorithms; and
a hardware processor to apply at least one picked compression algorithm to incoming data items, wherein the picked compression algorithm to be applied to at least one individual data item from among the incoming data items is selected, from among the plural compression algorithms, by the hardware processor, depending at least on said individual data item,
wherein at least the processor is deployed in a communication system including at least one transmitter and at least one receiver, thereby to define a transmitter side of the system and a receiver side of the system, and
wherein the transmitter side of the system is implemented by a dedicated hardware accelerator,
wherein the hardware processor selects the picked compression algorithm using a machine learning model and employs a best-ness criterion which is a function of plural characteristics of each compression algorithm and wherein the plural characteristics are all normalized to a single range and then combined using a combination function.