Search for: All records

The complexity of manycore System-on-chips (SoCs) is growing faster than our ability to manage them to reduce the overall energy consumption. Further, as SoC design moves towards 3D-architectures, the core's power density increases leading to unacceptable high peak chip temperatures. In this paper, we consider the optimization problem of dynamic power management (DPM) in manycore SoCs for an allowable performance penalty (say 5%) and admissible peak chip temperature. We employ a machine learning (ML) based DPM policy, which selects the voltage/frequency (V/F) levels for different cluster of cores as a function of the application workload features such as core computation and inter-core traffic etc. We propose a novel learning-to-search (L2S) framework to automatically identify an optimized sequence of DPM decisions from a large combinatorial space for joint energy-thermal optimization for one or more given applications. The optimized DPM decisions are given to a supervised learning algorithm to train a DPM policy, which mimics the corresponding decision-making behavior. Our experiments on two different manycore architectures designed using wireless interconnect and monolithic 3D demonstrate that principles behind the L2S framework are applicable for more than one configuration. Moreover, L2S-based DPM policies achieve up to 30 energy-delay product savings and reduce the peak chip temperature by up to 17 °C compared to the state-of-the-art ML methods for an allowable performance overhead of only 5 . 

Manycore GPU architectures have become the mainstay for accelerating graph computations. One of the primary bottlenecks to performance of graph computations on manycore architectures is the data movement. Since most of the accesses in graph processing are due to vertex neighborhood lookups, locality in graph data structures plays a key role in dictating the degree of data movement. Vertex reordering is a widely used technique to improve data locality within graph data structures. However, these reordering schemes alone are not sufficient as they need to be complemented with efficient task allocation on manycore GPU architectures to reduce latency due to local cache misses. Consequently, in this article, we introduce a software/hardware co-design framework for accelerating graph computations. Our approach couples an architecture-aware vertex reordering with a priority-based task allocation technique. As the task allocation aims to reduce on-chip latency and associated energy, the choice of Network-on-Chip (NoC) as the communication backbone in the manycore platform is an important parameter. By leveraging emerging three-dimensional (3D) integration technology, we propose design of a small-world NoC (SWNoC)-enabled manycore GPU architecture, where the placement of the links connecting the streaming multiprocessors (SMs) and the memory controllers (MCs) follow a power-law distribution. The proposed 3D SWNoC-enabled software/hardware co-design framework achieves 11.1% to 22.9% performance improvement and 16.4% to 32.6% less energy consumption depending on the dataset and the graph application, when compared to the default order of dataset running on a conventional planar mesh architecture. 

With the increasing complexity of highly integrated system on chips (SoCs), the power management system (PMS) is required to provide several power supplies efficiently for individual blocks. This paper presents a single-inductor multiple outputs (SIMO) an inductor-first hybrid converter that generates three outputs between 0.4V and 1.6V from a 1.8V input. The proposed multiple-output hybrid power stage can improve the conversion efficiency by reducing inductor current while extending the output voltage range compared with the existing hybrid topologies. In addition, the proposed converter employs an on-chip switched-capacitor power stage (SCPS) with a dual switching frequency technique, resulting in a fast response time, low cross-regulation, and reduced number of on-chip pads. Measurement results show that the converter achieves a peak efficiency of 87.5% with a maximum output current of 450mA. The converter is integrated with a fast voltage regulation loop with a 500MHz system clock to achieve less than 0.01mA/mV cross-regulation and a maximum 20mV overshoot at full-load transient response. The design is fabricated in the standard 180nm CMOS technology 

Graph application workloads are dominated by random memory accesses with poor locality. To tackle the irregular and sparse nature of computation, ReRAM-based Processing-in-Memory (PIM) architectures have been proposed recently. Most of these ReRAM architecture designs have focused on mapping graph computations into a set of multiply-and-accumulate (MAC) operations. ReRAMs also offer a key advantage in reducing memory latency between cores and memory by allowing for processing-in-memory (PIM). However, when implemented on a ReRAM-based manycore architecture, graph applications still pose two key challenges – significant storage requirements (particularly due to wasted zero cell storage), and significant amount of on-chip traffic. To tackle these two challenges, in this paper we propose the design of a 3D NoC-enabled ReRAM-based manycore architecture. Our proposed architecture incorporates a novel crossbar-aware node reordering to reduce ReRAM storage requirements. Secondly, its 3D NoC-enabled design reduces on-chip communication latency. Our architecture outperforms the state-of-the-art in ReRAM-based graph acceleration by up to 5x in performance while consuming up to 10.3x less energy for a range of graph inputs and workloads.