Impact of Multi-level Clustering on Performance Driven Global Placement

Delay and wirelength minimization continue to be important objectives in the design of high-performance computing systems. For large-scale circuits, the clustering process becomes essential for reducing the problem size. However, to the best of our knowledge, there is no study about the impact of multi-level clustering on performance-driven global placement. In this paper, five clustering algorithms including the quasi-optimal retiming delay driven PRIME and the cutsize-driven ESC have been considered for their impact on state-of-the-art mincut based global placement. Results show that minimizing cutsize or wirelength during clustering typically results in significant performance improvements

Topology-Based Performance Analysis and Optimization of Latency-Insensitive Systems

Latency-insensitive protocols allow system-on-chip engineers to decouple the design of the computing cores from the design of the inter-core communication channels while following the synchronous design paradigm. In a latency-insensitive system (LIS) each core is encapsulated within a shell, a synthesized interface module that dynamically controls its operation. At each clock period, if new data has not arrived on an input channel or a stalling request has arrived on an output channel, the shell stalls the core and buffers other incoming valid data for future processing. The combination of finite buffers and backpressure from stalling can cause throughput degradation. Previous works addressed this problem by increasing buffer space to reduce the backpressure requests or inserting extra buffering to balance the channel latency around a LIS. We explore the theoretical complexity of these approaches and propose a heuristic algorithm for efficient queue sizing. We also practically characterize several LIS topologies and how the topology of a LIS can impact not only how much throughput degradation will occur, but also the difficulty of finding optimal queue sizing solutions