Three-dimensional Networks-on-Chips (3D-NoCs) have emerged as an alternative to further enhance the performance, functionality, and packaging density of 2D-NoCs. However, the increasing complexity of NoC routers, the continuous miniaturization of silicon technology, the lower operating voltages, and the higher operating frequencies have made the NoC increasingly vulnerable to soft errors. In particular, transient faults occurring in the route computation unit (RCU) can provoke misrouting which may lead to severe effects such as deadlocks or packet loss, corrupting the operation of the entire chip. By combining a reliable fault detection circuit leveraging circuit-level double-sampling, with a cost-effective rerouting mechanism, we develop a full fault-tolerance solution that can efficiently detect and correct such fatal errors before the affected packets leave the router. To validate the proposed solution, we also introduce a novel method for simulation-based fault-injection based on the NoC's gate-level netlist. Experimental results obtained from a partially and vertically connected 3D-NoC indicate that our solution can provide a high level of reliability in the presence of errors, at the expense of an area and power overhead of 4.1% and 6.8% respectively.

3D integration opens up new opportunities for future multiprocessor chips by enabling fast and highly scalable 3D Network-on-Chip (NoC) topologies. However, in an aim to reduce the cost of Through-silicon via (TSV), partially vertically connected NoCs, in which only a few vertical TSV links are available, have been gaining relevance. To reliably route packets under such conditions, we introduce a lightweight, efficient and highly resilient adaptive routing algorithm targeting partially vertically connected 3D-NoCs named First-Last. It requires a very low number of virtual channels (VCs) to achieve deadlock-freedom (2 VCs in the East and North directions and 1 VC in all other directions), and guarantees packet delivery as long as one healthy TSV connecting all layers is available anywhere in the network. An improved version of our algorithm, named Enhanced-First-Last is also introduced and shown to dramatically improve performance under low TSV availability while still using less virtual channels than state-of-the-art algorithms. A comprehensive evaluation of the cost and performance of our algorithms is performed to demonstrate their merits with respects to existing solutions.

As the number of processing elements in modern chips keeps increasing, the evaluation of new designs will need to account for various challenges at the NoC level. To cope with the impractically long run times when simulating large NoCs, we introduce a novel GPU-based parallel simulation method that can speed up simulations by over 250×, while offering RTL-like accuracy. These promising results make our simulation method ideal for evaluating future NoCs comprising thousands of nodes.

3D integration opens up new opportunities for future multiprocessor chips by enabling fast and highly scalable 3D Network-on-Chip (NoC) topologies. However, in an aim to reduce the cost of Through-silicon via (TSV), partially vertically connected NoCs, in which only a few vertical TSV links are available, have been gaining relevance. In addition, the number of vertical paths can be expected to be further reduced due to defects and runtime failures. To reliably route packets under such conditions, we introduce a lightweight, efficient and highly resilient adaptive routing algorithm targeting partially vertically connected 3D-NoCs named “Rout3D”. It requires a very low number of virtual channels (VCs) to achieve deadlock-freedom (2 VCs in the East and North directions and 1 VC in all other directions), and guarantees packet delivery as long as one healthy TSV connecting all layers is available anywhere in the network. We combine our algorithm with a novel offline reconfiguration method requiring only 4 bits per router to maintain connectivity upon the occurrence of faults while minimizing the implementation cost. Simulation results reveal that our algorithm is capable of sustaining a very good level of performance compared to related works, in spite of using less virtual channels.