Artificial intelligence and machine learning are experiencing widespread adoption in the industry, academia, and even public consciousness. This has been driven by the rapid advances in the applications and accuracy of AI through increasingly complex algorithms and models; this, in turn, has spurred research into developing specialized hardware AI accelerators. The rapid pace of the advances makes it easy to miss the forest for the trees: they are often developed and evaluated in a vacuum without considering the full application environment in which they must eventually operate. In this paper, we deploy and characterize Face Recognition, an AI-centric edge video analytics application built using open source and widely adopted infrastructure and ML tools. We evaluate its holistic, end-to-end behavior in a production-size edge data center and reveal the “AI tax” for all the processing that is involved. Even though the application is built around state-of-the-art AI and ML algorithms, it relies heavily on pre- and post-processing code which must be executed on a general-purpose CPU. As AI-centric applications start to reap the acceleration promised by so many accelerators, we find they impose stresses on the underlying software infrastructure and the data center’s capabilities: storage and network bandwidth become major bottlenecks with increasing AI acceleration. By not having to serve a wide variety of applications, we show that a purpose-built edge data center can be designed to accommodate the stresses of accelerated AI at 15% lower TCO than one de-rived from homogeneous servers and infrastructure. We also discuss how our conclusions generalize beyond Face Recognition as many AI-centric applications at the edge rely upon the same underlying software and hardware infrastructure.
With the slowing of technology scaling, the only known way to further improve computer system performance under energy constraints is to employ hardware accelerators. Already today, many chips in m obile, edge and cloud computing concurrently employ multiple accelerators in what we call accelerator-level parallelism (ALP). For the needed benefits of ALP to spread to computer systems more broadly, we herein charge the community to develop better “best practices” for: targeting accelerators, managing accelerator concurrency, choreographing inter-accelerator communication, and productively programming accelerators.
We introduce Air Learning, an AI research platform for benchmarking algorithm-hardware performance and energy efficiency trade-offs. We focus in particular on deep reinforcement learning (RL) interactions in autonomous unmanned aerial vehicles (UAVs). Equipped with a random environment generator, AirLearning exposes a UAV to a diverse set of challenging scenarios. Users can specify a task, train different RL policies and evaluate their performance and energy efficiency on a variety of hardware platforms. To show how Air Learning can be used, we seed it with Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) to solve a point-to-point obstacle avoidance task in three different environments, generated using our configurable environment generator. We train the two algorithms using curriculum learning and non-curriculum-learning. Air Learning assesses the trained policies' performance, under a variety of quality-of-flight (QoF) metrics, such as the energy consumed, endurance and the average trajectory length, on resource-constrained embedded platforms like a Ras-Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to 79.43% longer trajectories in one of the environments. To understand the source of such differences, we use Air Learning to artificially degrade desktop performance to mimic what happens on a low-end embedded system. QoF metrics with hardware-in-the-loop characterize those differences and expose how the choice of onboard compute affects the aerial robot's performance. We also conduct reliability studies to demonstrate how Air Learning can help understand how sensor failures affect the learned policies. All put together, Air Learning enables a broad class of RL studies on UAVs. More information and code for Air Learning can be found here.
Accelerators are becoming popular owing to their exceptional performance and power-efficiency. However, researchers are yet to pay close attention to their reliability---a key challenge as technology scaling makes building reliable systems challenging. A straightforward solution to make accelerators reliable is to design the accelerator from the ground-up to be reliable by itself. However, such a myopic view of the system, where each accelerator is designed in isolation, is unsustainable as the number of integrated accelerators continues to rise in SoCs. To address this challenge, we propose a paradigm called "asymmetric resilience'' that avoids accelerator-specific reliability design. Instead, its core principle is to develop the reliable heterogeneous system around the CPU architecture. We explain the implications of architecting such a system and the modifications needed in a heterogeneous system to adopt such an approach. As an example, we demonstrate how to use asymmetric resilience to handle GPU execution errors using the CPU with minimal overhead. The general principles can be extended to include other accelerators.
The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and large-scale. Machine learning, or more specifically deep reinforcement learning (DRL), methods have been proposed widely to address these issues. By incorporating deep learning into traditional RL, DRL is highly capable of solving complex, dynamic, and especially high-dimensional cyber defense problems. This paper presents a survey of DRL approaches developed for cyber security. We touch on different vital aspects, including DRL-based security methods for cyber-physical systems, autonomous intrusion detection techniques, and multi-agent DRL-based game theory simulations for defense strategies against cyber attacks. Extensive discussions and future research directions on DRL-based cyber security are also given. We expect that this comprehensive review provides the foundations for and facilitates future studies on exploring the potential of emerging DRL to cope with increasingly complex cyber security problems.
Active Timing Margin (ATM) is a technology that improves processor efficiency by reducing the pipeline timing margin with a control loop that adjusts voltage and frequency based on real-time chip environment monitoring. Although ATM has already been shown to yield substantial performance benefits, its full potential has yet to be unlocked. In this paper, we investigate how to maximize ATM’s efficiency gain with a new means of exposing the inter-core speed variation: finetuning the ATM control loop. We conduct our analysis and evaluation on a production-grade POWER7+ system. On the POWER7+ server platform, we fine-tune the ATM control loop by programming its Critical Path Monitors, a key component of its ATM design that measures the cores’ timing margins. With a robust stress-test procedure, we expose over 200 MHz of inherent inter-core speed differential by fine-tuning the percore ATM control loop. Exploiting this differential, we manage to double the ATM frequency gain over the static timing margin; this is not possible using conventional means, i.e. by setting fixed points for each core, because the corelevel must account for chip-wide worst-case voltage variation. To manage the significant performance heterogeneity of fine-tuned systems, we propose application scheduling and throttling to manage the chip’s process and voltage variation. Our proposal improves application performance by more than 10% over the static margin, almost doubling the 6% improvement of the default, unmanaged ATM system. Our technique is general enough that it can be adopted by any system that employs an active timing margin control loop.
Keywords-Active timing margin, Performance, Power efficiency, Reliability, Critical path monitors
Over a billion mobile consumer system-on-chip (SoC) chipsets ship each year. Of these, the mobile consumer market undoubtedly involving smartphones has a significant market share. Most modern smartphones comprise of advanced SoC architectures that are made up of multiple cores, GPS, and many different programmable and fixed-function accelerators connected via a complex hierarchy of interconnects with the goal of running a dozen or more critical software usecases under strict power, thermal and energy constraints. The steadily growing complexity of a modern SoC challenges hardware computer architects on how best to do early stage ideation. Late SoC design typically relies on detailed full-system simulation once the hardware is specified and accelerator software is written or ported. However, early-stage SoC design must often select accelerators before a single line of software is written. To help frame SoC thinking and guide early stage mobile SoC design, in this paper we contribute the Gables model that refines and retargets the Roofline model—designed originally for the performance and bandwidth limits of a multicore chip—to model each accelerator on a SoC, to apportion work concurrently among different accelerators (justified by our usecase analysis), and calculate a SoC performance upper bound. We evaluate the Gables model with an existing SoC and develop several extensions that allow Gables to inform early stage mobile SoC design.
Index Terms—Accelerator architectures, Mobile computing, Processor architecture, System-on-Chip
Modern large-scale computing systems (data centers, supercomputers, cloud and edge setups and high-end cyber-physical systems) employ heterogeneous architectures that consist of multicore CPUs, general-purpose many-core GPUs, and programmable FPGAs. The effective utilization of these architectures poses several challenges, among which a primary one is power consumption. Voltage reduction is one of the most efficient methods to reduce power consumption of a chip. With the galloping adoption of hardware accelerators (i.e., GPUs and FPGAs) in large datacenters and other large-scale computing infrastructures, a comprehensive evaluation of the safe voltage reduction levels for each different chip can be employed for efficient reduction of the total power. We present a survey of recent studies in voltage margins reduction at the system level for modern CPUs, GPUs and FPGAs. The pessimistic voltage guardbands inserted by the silicon vendors can be exploited in all devices for significant power savings. Voltage reduction can reach 12% in multicore CPUs, 20% in manycore GPUs and 39% in FPGAs.
Today's cloud service architectures follow a “one size fits all” deployment strategy where the same service version instantiation is provided to the end users. However, consumers are broad and different applications have different accuracy and responsiveness requirements, which as we demonstrate renders the “one size fits all” approach inefficient in practice. We use a production grade speech recognition engine, which serves several thousands of users, and an open source computer vision based system, to explain our point. To overcome the limitations of the “one size fits all” approach, we recommend Tolerance Tiers where each MLaaS tier exposes an accuracy/responsiveness characteristic, and consumers can programmatically select a tier. We evaluate our proposal on the CPU-based automatic speech recognition (ASR) engine and cutting-edge neural networks for image classification deployed on both CPUs and GPUs. The results show that our proposed approach provides a MLaaS cloud service architecture that can be tuned by the end API user or consumer to outperform the conventional “one size fits all” approach.
Cloud-based Web services are shifting to the event-driven, scripting language-based programming model to achieve productivity, flexibility, and scalability. Implementations of this model, however, generally suffer from long tail latencies, which we measure using Node.js as a case study. Unlike in traditional thread-based systems, reducing long tails is difficult in event-driven systems due to their inherent asynchronous programming model. We propose a framework to identify and optimize tail latency sources in scripted eventdriven Web services. We introduce profiling that allows us to gain deep insights into not only how asynchronous eventdriven execution impacts application tail latency but also how the managed runtime system overhead exacerbates the tail latency issue further. Using the profiling framework, we propose an event-driven execution runtime design that orchestrates the hardware’s boosting capabilities to reduce tail latency. We achieve higher tail latency reductions with lower energy overhead than prior techniques that are unaware of the underlying event-driven program execution model. The lessons we derive from Node.js apply to other event-driven services based on scripting language frameworks.
We develop AirLearning, a tool suite for endto-end closed-loop UAV analysis, equipped with a customized yet randomized environment generator in order to expose the UAV with a diverse set of challenges. We take Deep Q networks (DQN) as an example deep reinforcement learning algorithm and use curriculum learning to train a point to point obstacle avoidance policy. While we determine the best policy based on the success rate, we evaluate it under strict resource constraints on an embedded platform such as RasPi 3. Using hardware in the loop methodology, we quantify the policy’s performance with quality of flight metrics such as energy consumed, endurance and the average length of the trajectory. We find that the trajectories produced on the embedded platform are very different from those predicted on the desktop, resulting in up to 26.43% longer trajectories.
Quality of flight metrics with hardware in the loop characterizes those differences in simulation, thereby exposing how the choice of onboard compute contributes to shortening or widening of ‘Sim2Real’ gap.
Deep neural networks have been extensively adopted for a myriad of applications due to their ability to learn patterns from large amounts of data. The desire to preserve user privacy and reduce user-perceived latency has created the need to perform deep neural network inference tasks on low-power consumer edge devices. Since such tasks often tend to be computationally intensive, offloading this compute from mobile/embedded CPU to a purposedesigned "Neural Processing Engines" is a commonly adopted solution for accelerating deep learning computations. While these accelerators offer significant speed-ups for key machine learning kernels, overheads resulting from frequent host-accelerator communication often diminish the net application-level benefit of this heterogeneous system. Our solution for accelerating such workloads involves developing ISA extensions customized for machine learning kernels and designing a custom in-pipeline execution unit for these specialized instructions. We base our ISA extensions on RISC-V: an open ISA specification that lends itself to such specializations. In this paper, we present the software infrastructure for optimizing neural network execution on RISC-V with ISA extensions. Our ISA extensions are derived from the RISC-V Vector ISA proposal, and we develop optimized implementations of the critical kernels such as convolution and matrix multiplication using these instructions. These optimized functions are subsequently added to the TensorFlow Lite source code and cross-compiled for RISC-V. We find that only a small set of instruction extensions achieves coverage over a wide variety of deep neural networks designed for vision and speech-related tasks. On average, our software implementation using the extended instructions set reduces the executed instruction count by 8X in comparison to baseline implementation. In parallel, we are also working on the hardware design of the inpipeline machine learning accelerator. We plan to open-source our software modifications to TF Lite, as well as the micro-architecture design in due course.
Big data, specifically data analytics, is responsible for driving many of consumers’ most common online activities, including shopping, web searches, and interactions on social media. In this paper, we present the first (micro)architectural investigation of a new industry-standard, open source benchmark suite directed at big data analytics applications—TPCx-BB (BigBench). Where previous work has usually studied benchmarks which oversimplify big data analytics, our study of BigBench reveals that there is immense diversity among applications, owing to their varied data types, computational paradigms, and analyses. In our analysis, we also make an important discovery generally restricting processor performance in big data. Contrary to conventional wisdom that big data applications lend themselves naturally to parallelism, we discover that they lack sufficient thread-level parallelism (TLP) to fully utilize all cores. In other words, they are constrained by Amdahl’s law. While TLP may be limited by various factors, ultimately we find that single-thread performance is as relevant in scale-out workloads as it is in more classical applications. To this end we present core packing: a software and hardware solution that could provide as much as 20% execution speedup for some big data analytics applications.
our contributions are as follows: • We investigate DSA and characterize the effectiveness of category-awareness. • We conduct a limit study to understand the benefit of applying approximation in a perframe manner with category-awareness (category-aware dynamic DSA). • We present the challenges of harnessing DSA and a proof-of-concept runtime.
Voltage stacking (VS) fundamentally improves power delivery efficiency (PDE) by series-stacking multiple voltage domains to eliminate explicit step-down voltage conversion and reduce energy loss along the power delivery path. However, it suffers from aggravated supply noise, preventing its adoption in mainstream computing systems. In this paper, we investigate a practical approach to enabling efficient and reliable power delivery in voltage-stacked manycore systems that can ensure worst-case supply noise reliability without excessive costly over-design. We start by developing an analytical model to capture the essential noise behaviors in VS. It allows us to identify dominant noise contributor and derive the worst-case conditions. With this in-depth understanding, we propose a hybrid voltage regulation solution to effectively mitigate noise with worst-case guarantees. When evaluated with real-world benchmarks, our solution can achieve 93.8% power delivery efficiency, an improvement of 13.9% over the conventional baseline.
Unmanned Aerial Vehicles (UAVs) are getting closer to becoming ubiquitous in everyday life. Among them, Micro Aerial Vehicles (MAVs) have seen an outburst of attention recently, specifically in the area with a demand for autonomy. A key challenge standing in the way of making MAVs autonomous is that researchers lack the comprehensive understanding of how performance, power, and computational bottlenecks affect MAV applications. MAVs must operate under a stringent power budget, which severely limits their flight endurance time. As such, there is a need for new tools, benchmarks, and methodologies to foster the systematic development of autonomous MAVs. In this paper, we introduce the “MAVBench” framework which consists of a closed-loop simulator and an end-to-end application benchmark suite. A closed-loop simulation platform is needed to probe and understand the intra-system (application data flow) and inter-system (system and environment) interactions in MAV applications to pinpoint bottlenecks and identify opportunities for hardware and software co-design and optimization. In addition to the simulator, MAVBench provides a benchmark suite, the first of its kind, consisting of a variety of MAV applications designed to enable computer architects to perform characterization and develop future aerial computing systems. Using our open source, end-to-end experimental platform, we uncover a hidden, and thus far unexpected compute to total system energy relationship in MAVs. Furthermore, we explore the role of compute by presenting three case studies targeting performance, energy and reliability. These studies confirm that an efficient system design can improve MAV’s battery consumption by up to 1.8X.
Mobile computing has grown drastically over the past decade. Despite the rapid pace of advancements, mobile device understanding, benchmarking, and evaluation are still in their infancies, both in industry and academia. This article presents an industry perspective on the challenges facing mobile computer architecture, specifically involving mobile workloads, benchmarking, and experimental methodology, with the hope of fostering new research within the community to address pending problems. These challenges pose a threat to the systematic development of future mobile systems, which, if addressed, can elevate the entire mobile ecosystem to the next level.
Mobile devices have come a long way from the first portable cellular phone developed by Motorola in 1973. Most modern smartphones are good enough to replace desktop computers. A smartphone today has enough computing power to be on par with the fastest supercomputers from the 1990s.
For instance, the Qualcomm Adreno 540 GPU found in the latest smartphones has a peak compute capability of more than 500 Gflops, putting it in competition with supercomputers that were on the TOP500 list in the early to mid-1990s. Mobile computing has experienced an unparalleled level of growth over the past decade. At the time of this writing, there are more than 2 billion mobile devices in the world.1 But perhaps even more importantly, mobile phones are showing no signs of slowing in uptake. In fact, smartphone adoption rates are on the rise. The number of devices is rising as mobile device penetration increases in markets like India and China. It is anticipated that the number of mobile subscribers will grow past 6 billion in the coming years.2 As Figure 1 shows, while the Western European and North American markets are reaching saturation, the vast majority of growth is coming from countries in Asia. Given that only 35 percent of the world’s population has thus far adopted mobile technology, there is still significant room for growth and innovation.
Unmanned Aerial Vehicles (UAVs) are getting closer to becoming ubiquitous in everyday life. Although the researchers in the robotic domain have made rapid progress in recent years, hardware and software architects in the computer architecture community lack the comprehensive understanding of how performance, power, and computational bottlenecks affect UAV applications. Such an understanding enables system architects to design microchips tailored for aerial agents. This paper is an attempt by computer architects to initiate the discussion between the two academic domains by investigating the underlying compute systems’ impact on aerial robotic applications. To do so, we identify performance and energy constraints and examine the impact of various compute knobs such as processor cores and frequency on these constraints. Our experiment show that such knobs allow for up to 5X speed up for a wide class of applications.
Recent advances in cognitive computing have brought widespread excitement for various machine learning–based intelligent services, ranging from autonomous vehicles to smart traffic-light systems. To push such cognitive services closer to reality, recent research has focused extensively on improving the performance, energy efficiency, privacy, and security of cognitive computing platforms.
Among all the issues, a rapidly rising and critical challenge to address is the practice of safe cognitive computing— that is, how to architect machine learning–based systems to be robust against uncertainty and failure to guarantee that they perform as intended without causing harmful behavior. Addressing the safety issue will involve close collaboration among different computing communities, and we believe computer architects must play a key role. In this position paper, we first discuss the meaning of safety and the severe implications of the safety issue in cognitive computing. We then provide a framework to reason about safety, and we outline several opportunities for the architecture community to help make cognitive computing safer.