US 12,238,126 B2
Systems and methods for learning-based high-performance, energy-efficient, and secure on-chip communication design framework
Ke Wang, Alexandria, VA (US); Hao Zheng, Arlington, VA (US); and Ahmed Louri, Vienna, VA (US)
Assigned to The George Washington University, Washington, DC (US)
Filed by The George Washington University, Washington, DC (US)
Filed on May 4, 2021, as Appl. No. 17/307,563.
Claims priority of provisional application 63/019,720, filed on May 4, 2020.
Prior Publication US 2021/0342690 A1, Nov. 4, 2021
Int. Cl. H04L 67/12 (2022.01); G06F 18/21 (2023.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); H04L 9/40 (2022.01)
CPC H04L 63/1425 (2013.01) [G06F 18/217 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); H04L 63/1441 (2013.01); H04L 67/12 (2013.01); G05B 2219/25198 (2013.01)] 17 Claims
OG exemplary drawing
 
1. An on-chip router comprising:
an on-chip input port receiving input data packets;
an on-chip output port transmitting output data packets;
an on-chip dynamic error detection and correction circuit having a decoder at said on-chip input port to decode and detect error at the input data packets, and an encoder at said on-chip output port to apply an error correction to the encoded data packets and output the output data packets;
an on-chip artificial neural network module;
an on-chip controller, wherein the controller instructs the artificial neural network module to detect malicious circuit modifications in the router, wherein said malicious circuit modifications comprise transient faults in the input data packets; and
a bypass channel that directly connects said on-chip input port to said on-chip output port and bypass said on-chip dynamic error detection and correction circuit,
wherein the artificial neural network module detects malicious circuit modifications by recognizing abnormal network behaviors caused by the transient faults, and
wherein the artificial neural network module applies one or more machine learning algorithms to detect the malicious circuit modifications.