UpTime: Towards Flow-based In-Memory Computing with High Fault-Tolerance

Processing in-memory promises to accelerate data-intensive applications by breaking von-Neumann based design principles. Flow-based computing is an in-memory computing paradigm that has shown immense potential for executing Boolean logic. Unfortunately, the immature fabrication processes for nanoscale memristor crossbars still struggle with yield challenges and run-time defects, which may render the computing system non-functional. Even worse, no previous studies have investigated the fault-tolerance of flow-based computing systems, which could potentially limit the capabilities of the entire paradigm. In this paper, we propose the UpTime framework to provide guaranties on the functional correctness and to maximize the lifetime of flow-based computing systems. The framework utilizes data layout organization to mitigate errors from faults with known type and location. To handle defects occurring at run-time, we propose the use of an error detection signal that can be evaluated with low overhead. The experimental evaluation demonstrates that the UpTime framework is capable of guaranteeing functional correctness for an average of 15.24 years. The up-time to down-time ratio is 99.9992%. Compared with utilizing the state-of-the-art write-verify scheme, the proposed error signal reduces power consumption by 25% and increases throughput by 6%, respectively.