Scalable Hierarchical Instruction Cache for Ultra-Low-Power Processors Clusters

TL;DR

This paper introduces a scalable hierarchical instruction cache for ultra-low-power processor clusters, improving performance and scalability while maintaining energy efficiency, suitable for IoT end-nodes.

Contribution

It proposes a novel two-level instruction cache with a shared L1.5 cache and a prefetcher, enhancing scalability and performance in ultra-low-power clusters.

Findings

Up to 20% higher operating frequency.

Maximum performance improved by up to 17%.

Maintains similar energy efficiency for key applications.

Abstract

High Performance and Energy Efficiency are critical requirements for Internet of Things (IoT) end-nodes. Exploiting tightly-coupled clusters of programmable processors (CMPs) has recently emerged as a suitable solution to address this challenge. One of the main bottlenecks limiting the performance and energy efficiency of these systems is the instruction cache architecture due to its criticality in terms of timing (i.e., maximum operating frequency), bandwidth, and power. We propose a hierarchical instruction cache tailored to ultra-low-power tightly-coupled processor clusters where a relatively large cache (L1.5) is shared by L1 private caches through a two-cycle latency interconnect. To address the performance loss caused by the L1 capacity misses, we introduce a next-line prefetcher with cache probe filtering (CPF) from L1 to L1.5. We optimize the core instruction fetch (IF) stage by removing the critical core-to-L1 combinational path. We present a detailed comparison of instruction cache architectures' performance and energy efficiency for parallel ultra-low-power (ULP) clusters. Focusing on the implementation, our two-level instruction cache provides better scalability than existing shared caches, delivering up to 20\% higher operating frequency. On average, the proposed two-level cache improves maximum performance by up to 17\% compared to the state-of-the-art while delivering similar energy efficiency for most relevant applications.