Cocoa: Content-oriented configurable architecture based on highly-adaptive data transmission networks

In domain of parallel computation, most works focus on optimizing PE organization or memory hierarchy to pursue the maximum efficiency, while the importance of data contents has been overlooked for a long time. Actually for structured data, insights on data contents (i.e. values and locations within a structured form) can greatly benefit the computation performance, as fine-grained data manipulation can be performed. In this paper, we claim that by providing a flexible and adaptive data path, an efficient architecture with capability of fine-grained data manipulation can be built. Specifically, we propose COCOA, a novel content-oriented configurable architecture, which integrates multi-functional data reorganization networks in traditional computing scheme to handle the contents of data during the transmission path, so that they can be processed more efficiently. We evaluate COCOA on various problems: complex matrix algorithm (matrix inversion) and sparse DNN. The results indicates that COCOA is versatile enough to achieve high computation efficiency in both cases.