With the continuous improvement of on-chip integrated voltage regulators (IVRs) and fast, adaptive frequency control, dynamic voltage-frequency scaling (DVFS) transition times have shrunk from the microsecond to the nanosecond regime, providing immense opportunity to improve energy efficiency. The key to unlocking the continued improvement in V/f circuit technology is the creation of new, smarter DVFS mechanisms that better adapt to rapid fluctuations in workload demand.

The performance of graphics processing units (GPU) workloads can be sensitive to the various clock domains which are dynamically tunable in modern GPUs. In this work, we observe that GPU application performance is sensitive towards NoC clock frequencies and the sensitivity varies during the execution of GPU kernels. We note that this heterogeneity is not adapted well by traditional dynamic voltage frequency scaling (DVFS) techniques. To that end, we introduce DUB, Dynamic Underclocking and Bypassing technique, for such heterogeneous GPU workloads. We enable bypassing re-timer flops and routers while underclocking the NoC frequency thus enabling high power savings at minimal performance loss. Compared to baseline we observe a 26% improvement in power savings with only 3% degradation in performance beating oracular DVFS techniques.

Recent advances in die-stacking and 2.5D chip integration technologies introduce in-package network heterogeneities that can complicate the interconnect design. Integrating chiplets over a silicon interposer offers new opportunities of optimizing interposer topologies. However, limited by the capability of existing network-on-chip (NoC) simulators, the full potential of the interposer-based NoCs has not been exploited. In this paper, we address the shortfalls of prior NoC designs and present a new family of chiplet topologies called Kite. Kite topologies better utilize the diverse networking and frequency domains existing in new interposer systems and outperform the prior chiplet topology proposals. Kite decreased synthetic traffic latency by 7% and improved the maximum throughput by 17% on average versus Double Butterfly and Butter Donut, two previous proposals developed using less accurate modeling.

Recent studies have shown the potential of last-level TLBs shared by multiple cores in tackling memory translation performance challenges posed by "big data" workloads. A key stumbling block hindering their effectiveness, however, is their high access time. We present a design methodology to reduce these high access times so as to realize high-performance and scalable shared L2 TLBs. As a first step, we study the benefits of replacing monolithic shared TLBs with a distributed set of small TLB slices. While this approach does reduce TLB lookup latency, it increases interconnect delays in accessing remote slices. Therefore, as a second step, we devise a lightweight single-cycle interconnect among the TLB slices by tailoring wires and switches to the unique communication characteristics of memory translation requests and responses. Our approach, which we dub NOCSTAR (NOCs for scalable TLB architecture), combines the high hit rates of shared TLBs with low access times of private L2 TLBs, enabling significant system performance benefits.