Ujjwal Gupta

Adaptive performance prediction for integrated GPUs

Integrated GPUs have become an indispensable component of mobile processors due to the increasing popularity of graphics applications. The GPU frequency is a key factor both in application throughput and mobile processor power consumption under graphics workloads. Therefore, dynamic power management algorithms have to assess the performance sensitivity to the GPU frequency accurately. Since the impact of the GPU frequency on performance varies rapidly over time, there is a need for online performance models that can adapt to varying workloads. This paper presents a light-weight adaptive runtime performance model that predicts the frame processing time. We use this model to estimate the frame time sensitivity to the GPU frequency. Our experiments on a mobile platform running common GPU benchmarks show that the mean absolute percentage error in frame time and frame time sensitivity prediction are 3.8% and 3.9%, respectively.

A generic energy optimization framework for heterogeneous platforms using scaling models

Mobile platforms are becoming highly heterogeneous by combining a powerful multiprocessor system-on-a-chip (MpSoC) with numerous other resources, including display, memory, power management IC, battery and wireless modems into a compact package. Furthermore, the MpSoC itself is a heterogeneous resource that integrates many processing elements such as CPU cores, GPU, video, image, and audio processors. Platform energy consumption and responsiveness are two major considerations for mobile systems, since they determine the battery life and user satisfaction, respectively. As a result, energy minimization approaches targeting mobile computing need to consider the platform at various levels of granularity. In this paper, we first present power consumption, response time, and energy consumption models for mobile platforms. Using these models, we optimize the energy consumption of baseline platforms under power, response time, and thermal constraints with and without introducing new resources. Finally, we validate the proposed framework through experiments on Qualcomms Snapdragon 800 Mobile Development Platforms.

Robust Communication with IoT Devices using Wearable Brain Machine Interfaces

Proliferation of internet-of-things (IoT) will lead to scenarios where humans will interact with and control a variety of networked devices including sensors and actuators. Wearable brain-machine interfaces (BMI) can be a key enabler of this interaction for people with disabilities and limited motor skills. At the same time, BMI can improve the experience of healthy individuals significantly. However, state-of-the-art BMI systems have limited applicability as they are prone to errors even with sophisticated machine learning algorithms used for classifying the electroencephalogram (EEG) signals. We improve the reliability of BMI communication significantly by proposing two techniques at higher abstraction layers. Our first contribution is a command confirmation protocol that protects the brain-machine communication against false interpretations at run time. The second contribution is an off-line optimal event selection algorithm that identifies the most reliable subset of events supported by the target BMI system. The event selection is guided by novel user specific reliability metrics defined for the first time in this paper. Extensive experiments using a commercial BMI system demonstrate that the proposed techniques increase the communication robustness significantly, and reduce the time to complete a complex navigation task by 63% on average.

Dynamic Power Budgeting for Mobile Systems Running Graphics Workloads

Competitive graphics performance is crucial for the success of state-of-the-art mobile processors. High graphics performance comes at the cost of higher power consumption, which elevates the temperature due to limited cooling solutions. To avoid thermal violations, the system needs to operate within a power budget. Since the power budget is a shared resource, there is a strong demand for effective dynamic power budgeting techniques. This paper presents a novel technique to efficiently distribute the power budget among the CPU and GPU cores, while maximizing performance. The proposed technique is evaluated using a state-of-the-art mobile platform using industrial benchmarks, and an in-house simulator. The experiments on the mobile platform show up to 15% increase in average frame rate compared to default power allocation algorithms.

Constrained Energy Optimizationin Heterogeneous Platforms UsingGeneralized Scaling Models

Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. We first present models for power consumption, response time and energy consumption of heterogeneous mobile platforms. Then, we use these models to optimize the energy consumption of baseline platforms under response time and temperature constraints with and without introducing new resources. We show that the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone.

Extending networks from chips to flexible and stretchable electronics

Emerging flexible hybrid electronics paradigm integrates traditional rigid integrated circuits and printed electronics on a flexible substrate. This hybrid approach aims to combine the physical benefits of flexible electronics with the computational advantages of the silicon technology. In this paper, we discuss the possibility to implement a physically flexible system capable of sensing, computation and communication. We argue that this capability can transform personalized computing by enabling the next big leap forward in the form factor design, similar to the shift from desktop and laptop computers to hand-held devices. Designing this type of a comprehensive system requires integrating many flexible and rigid resources on the same substrate. As a result, efficient interconnection network design rises as one of the major challenges similar to the system-on-chip experience. Therefore, we also discuss the interconnect design challenges and promising solutions for flexible hybrid systems.

Multi-objective design optimization for flexible hybrid electronics

Flexible systems that can conform to any shape are desirable for wearable applications. Over the past decade, there have been tremendous advances in the domain of flexible electronics which enabled printing of devices, such as sensors on a flexible substrate. Despite these advances, pure flexible electronics systems are limited by poor performance and large feature sizes. Flexible hybrid electronics (FHE) is an emerging technology which addresses these issues by integrating high performance rigid integrated circuits and flexible devices. Yet, there are no system-level design flows and algorithms for the design of FHE systems. To this end, this paper presents a multi-objective design algorithm to implement a target application optimally using a library of rigid and flexible components. Our algorithm produces a set of Pareto frontiers that optimize the physical flexibility, energy per operation and area metrics. Simulation studies show a 32× range in area and 4× range in flexibility across the set of Pareto-optimal design points.

Can systems extend to polymer? SoP architecture design and challenges

Mechanically flexible and conformal shaped electronics is gaining momentum in todays electronics ecosystem. Rapid progress at device and circuit levels are already underway, but researchers are yet to envision the system design in a flexible form. This paper introduces hybrid flexible systems, and coins the term System-on-Polymer (SoP) to combine the advantages of flexible electronics and traditional silicon technology. First, we formally define flexibility as a new design metric in addition to existing power, performance, and area metrics. Then, we present a novel optimization approach to place rigid components on to a flexible substrate while minimizing the loss in flexibility. We show that intuitive placement leads to as much as 5.7x loss in flexibility compared to the optimal placement. Finally, we discuss major challenges in the architecture and design of SoPs.

STAFF: online learning with stabilized adaptive forgetting factor and feature selection algorithm

Dynamic resource management techniques rely on power consumption and performance models to optimize the operating frequency and utilization of processing elements, such as CPU and GPU. Despite the importance of these decisions, many existing approaches rely on fixed power and performance models that are learned offline. However, offline models cannot guarantee accuracy when workloads differ significantly from the training available at design time. This paper presents an online learning framework (STAFF) that constructs adaptive run-time models for stationary and non-stationary workloads. STAFF is the first framework that (1) guarantees stability while quickly adapting to workload changes, (2) performs online feature selection with linear complexity, and (3) adapts to new model coefficients by employing adaptively varying forgetting factor, all at the same time. Experiments on an Intel® Core™ i5 6th generation platform demonstrate up to 6× improvement in the performance prediction accuracy compared to existing techniques.

Adaptive Performance Sensitivity Model to Support GPU Power Management

Integrated graphics units consume a large portion of power in client and mobile systems. Pro-active power management algorithms have been devised to meet expected user experience while reducing energy consumption. These techniques often rely on power and performance sensitivity models that are constructed at design phase using a number of workloads. Despite this, the lack of representative workloads and model identification overhead adversely impact accuracy and development time, respectively. Conversely, two main challenges limit runtime post-design identification: the absence of sensitivity feedback from the system and the limited computational resources. We propose a two-stage methodology that first identifies the features of the sensitivity model offline by leveraging a reduced amount of training data and then uses recursive least square algorithm to fit and adapt the coefficients of the model to workload changes at runtime. The proposed adaptive approach can reduce offline training data by 50% with respect to full offline model identification while maintaining accuracy as much as 95% on average.