Neural Scheduling Algorithms for Time-Multiplex Switches

In an N × N time-multiplex switch, transmission conflict arises when two or more input adaptors transmit packets to the same output adaptor simultaneously. To resolve transmission conflict, we propose two neural-based scheduling algorithms which use a large number of simple processing elements to perform scheduling in parallel. The first algorithm uses N2 hystersis McCulloch-Pitts neurons to determine conflict-free transmission schedules with maximum throughput. The second algorithm resolves transmission conflict among the first M packets in each input queue. It determines suboptimal transmission schedules using only N M neurons (M < N). M is a design parameter: IfM is larger, we can find closer-to-optimal transmission schedules, but we need more neurons. Simulation results show that the first algorithm can find near-global optimal transmission schedules. The second algorithm can give close-to-optimal transmission schedules using only a small M. When N= 500 and M = 10, the throughput efficiency is already 96.44 % while the required number of neurons is reduced from N2 = 250000 to N M = 5 000.