Adaptive data compression for high-performance low-power on-chip networks

With the recent design shift towards increasing the number of processing elements in a chip, high-bandwidth support in on-chip interconnect is essential for low-latency communication. Much of the previous work has focused on router architectures and network topologies using wide/long channels. However, such solutions may result in a complicated router design and a high interconnect cost. In this paper, we exploit a table-based data compression technique, relying on value patterns in cache traffic. Compressing a large packet into a small one can increase the effective bandwidth of routers and links, while saving power due to reduced operations. The main challenges are providing a scalable implementation of tables and minimizing overhead of the compression latency. First, we propose a shared table scheme that needs one encoding and one decoding tables for each processing element, and a management protocol that does not require in-order delivery. Next, we present streamlined encoding that combines flit injection and encoding in a pipeline. Furthermore, data compression can be selectively applied to communication on congested paths only if compression improves performance. Simulation results in a 16-core CMP show that our compression method improves the packet latency by up to 44% with an average of 36% and reduces the network power consumption by 36% on average.