No Arabic abstract
We introduce ratatoskr, an open-source framework for in-depth power, performance and area (PPA) analysis in NoCs for 3D-integrated and heterogeneous System-on-Chips (SoCs). It covers all layers of abstraction by providing a NoC hardware implementation on RT level, a NoC simulator on cycle-accurate level and an application model on transaction level. By this comprehensive approach, ratatoskr can provide the following specific PPA analyses: Dynamic power of links can be measured within 2.4% accuracy of bit-level simulations while maintaining cycle-accurate simulation speed. Router power is determined from RT level synthesis combined with cycle-accurate simulations. The performance of the whole NoC can be measured both via cycle-accurate and RT level simulations. The performance of individual routers is obtained from RT level including gate-level verification. The NoC area is calculated from RT level. Despite these manifold features, ratatoskr offers easy two-step user interaction: First, a single point-of-entry that allows to set design parameters and second, PPA reports are generated automatically. For both the input and the output, different levels of abstraction can be chosen for high-level rapid network analysis or low-level improvement of architectural details. The synthesize NoC model reduces up to 32% total router power and 3% router area in comparison to a conventional standard router. As a forward-thinking and unique feature not found in other NoC PPA-measurement tools, ratatoskr supports heterogeneous 3D integration that is one of the most promising integration paradigms for upcoming SoCs. Thereby, ratatoskr lies the groundwork to design their communication architectures.
The current trend for domain-specific architectures (DSAs) has led to renewed interest in research test chips to demonstrate new specialized hardware. Tape-outs also offer huge pedagogical value garnered from real hands-on exposure to the whole system stack. However, successful tape-outs demand hard-earned experience, and the design process is time consuming and fraught with challenges. Therefore, custom chips have remained the preserve of a small number of research groups, typically focused on circuit design research. This paper describes the CHIPKIT framework. We describe a reusable SoC subsystem which provides basic IO, an on-chip programmable host, memory and peripherals. This subsystem can be readily extended with new IP blocks to generate custom test chips. We also present an agile RTL development flow, including a code generation tool calledVGEN. Finally, we outline best practices for full-chip validation across the entire design cycle.
Memories that exploit three-dimensional (3D)-stacking technology, which integrate memory and logic dies in a single stack, are becoming popular. These memories, such as Hybrid Memory Cube (HMC), utilize a network-on-chip (NoC) design for connecting their internal structural organizations. This novel usage of NoC, in addition to aiding processing-in-memory capabilities, enables numerous benefits such as high bandwidth and memory-level parallelism. However, the implications of NoCs on the characteristics of 3D-stacked memories in terms of memory access latency and bandwidth have not been fully explored. This paper addresses this knowledge gap by (i) characterizing an HMC prototype on the AC-510 accelerator board and revealing its access latency behaviors, and (ii) by investigating the implications of such behaviors on system and software designs.
Heterogeneous 3D System-on-Chips (3D SoCs) are the most promising design paradigm to combine sensing and computing within a single chip. A special characteristic of communication networks in heterogeneous 3D SoCs is the varying latency and throughput in each layer. As shown in this work, this variance drastically degrades the network performance. We contribute a co-design of routing algorithms and router microarchitecture that allows to overcome these performance limitations. We analyze the challenges of heterogeneity: Technology-aware models are proposed for communication and thereby identify layers in which packets are transmitted slower. The communication models are precise for latency and throughput under zero load. The technology model has an area error and a timing error of less than 7.4% for various commercial technologies from 90 to 28nm. Second, we demonstrate how to overcome limitations of heterogeneity by proposing two novel routing algorithms called Z+(XY)Z- and ZXYZ that enhance latency by up to 6.5x compared to conventional dimension order routing. Furthermore, we propose a high vertical-throughput router microarchitecture that is adjusted to the routing algorithms and that fully overcomes the limitations of slower layers. We achieve an increased throughput of 2 to 4x compared to a conventional router. Thereby, the dynamic power of routers is reduced by up to 41.1% and we achieve improved flit latency of up to 2.26x at small total router area costs between 2.1% and 10.4% for realistic technologies and application scenarios.
Human Activity Recognition (HAR), based on machine and deep learning algorithms is considered one of the most promising technologies to monitor professional and daily life activities for different categories of people (e.g., athletes, elderly, kids, employers) in order to provide a variety of services related, for example to well-being, empowering of technical performances, prevention of risky situation, and educational purposes. However, the analysis of the effectiveness and the efficiency of HAR methodologies suffers from the lack of a standard workflow, which might represent the baseline for the estimation of the quality of the developed pattern recognition models. This makes the comparison among different approaches a challenging task. In addition, researchers can make mistakes that, when not detected, definitely affect the achieved results. To mitigate such issues, this paper proposes an open-source automatic and highly configurable framework, named B-HAR, for the definition, standardization, and development of a baseline framework in order to evaluate and compare HAR methodologies. It implements the most popular data processing methods for data preparation and the most commonly used machine and deep learning pattern recognition models.
Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) projects without sharing sensitive data, such as, patient records, financial data, or classified secrets. Open Federated Learning (OpenFL https://github.com/intel/openfl) is an open-source framework for training ML algorithms using the data-private collaborative learning paradigm of FL. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and deep learning frameworks. Here, we summarize the motivation and development characteristics of OpenFL, with the intention of facilitating its application to existing ML model training in a production environment. Finally, we describe the first use of the OpenFL framework to train consensus ML models in a consortium of international healthcare organizations, as well as how it facilitates the first computational competition on FL.