Learning-based system-level power modeling of hardware IPs

Accurate power models for hardware components at high levels of abstraction are a critical component to enable system-level power analysis and optimization. Virtual platform prototypes are widely utilized to support early system-level design space exploration. There is, however, a lack of accurate and fast power models of hardware components at such high-levels of abstraction. In this dissertation, we present novel learning‑based approaches for extending fast functional simulation models of white-, gray-, and black-box custom hardware intellectual property components (IPs) with accurate power estimates. Depending on the observability, we extend high-level functional models with the capability to capture data-dependent resource, block, or I/O activity without a significant loss in simulation speed. We further leverage state-of-the-art machine learning techniques to synthesize abstract power models that can predict cycle-, block-, and invocation-level power from low-level hardware implementations, where we introduce novel structural decomposition techniques to reduce model complexities and increase estimation accuracy. Our white-box approach integrates with existing high-level synthesis (HLS) tools to automatically extract resource mapping information, which is used to trace data-dependent resource-level activity and drive a cycle-accurate online power-performance model during functional simulation. Our gray-box approach supports power estimation at coarser basic block granularity. It uses only limited information about block inputs and outputs to extract light-weight block-level activity from a functional simulation and drive a basic block-level power model that utilizes a control flow decomposition to improve accuracy and speed. It is faster than cycle-level models, while providing a finer granularity than invocation-level models, which allows to further navigate accuracy and speed trade-offs. We finally propose a novel approach for extending behavioral models of black-box hardware IPs with an invocation-level power estimate. Our black-box model only uses input and output history to track data-dependent pipeline behavior, where we introduce a specialized ensemble learning that is composed out of individually selected cycle-by-cycle models with reduced complexity and increased accuracy. The proposed approaches are fully automated by integrating with existing, commercial HLS tools for custom hardware synthesized by HLS. Results of applying our approaches to various industrial‑strength design examples show that our power models can predict cycle‑, basic block-, and invocation-level power consumption to within 10%, 9%, and 3% of a commercial gate-level power estimation tool, respectively, all while running at several order of magnitude faster speeds of 1-10Mcycles/sec.Electrical and Computer Engineerin

Power aware early design stage hardware software co-optimization

Co-optimizing hardware and software can lead to substantial performance and energy benefits, and is becoming an increasingly important design paradigm. In scientific computing, power constraints increasingly necessitate the return to specialized chips such as Intel’s MIC or IBM’s Blue-Gene architectures. To enable hardware/software co-design in early stages of the design cycle, we propose a simulation infrastructure methodology by combining high-abstraction performance simulation using Sniper with power modeling using McPAT and custom DRAM power models. Sniper/McPAT is fast — simulation speed is around 2 MIPS on an 8-core host machine — because it uses analytical modeling to abstract away core performance during multi-core simulation. We demonstrate Sniper/McPAT’s accuracy through validation against real hardware; we report average performance and power prediction errors of 22.1% and 8.3%, respectively, for a set of SPEComp benchmarks

Formal and Informal Methods for Multi-Core Design Space Exploration

We propose a tool-supported methodology for design-space exploration for embedded systems. It provides means to define high-level models of applications and multi-processor architectures and evaluate the performance of different deployment (mapping, scheduling) strategies while taking uncertainty into account. We argue that this extension of the scope of formal verification is important for the viability of the domain.Comment: In Proceedings QAPL 2014, arXiv:1406.156

Fast Power and Energy Efficiency Analysis of FPGA-based Wireless Base-band Processing

Nowadays, demands for high performance keep on increasing in the wireless communication domain. This leads to a consistent rise of the complexity and designing such systems has become a challenging task. In this context, energy efficiency is considered as a key topic, especially for embedded systems in which design space is often very constrained. In this paper, a fast and accurate power estimation approach for FPGA-based hardware systems is applied to a typical wireless communication system. It aims at providing power estimates of complete systems prior to their implementations. This is made possible by using a dedicated library of high-level models that are representative of hardware IPs. Based on high-level simulations, design space exploration is made a lot faster and easier. The definition of a scenario and the monitoring of IP's time-activities facilitate the comparison of several domain-specific systems. The proposed approach and its benefits are demonstrated through a typical use case in the wireless communication domain.Comment: Presented at HIP3ES, 201

Design Space Exploration in Cyber-Physical Systems

Cyber physical systems (CPS) integrate a variety of engineering areas such as control, mechanical and computer engineering in a holistic design effort. While interdependencies between the different disciplines are key attributes of CPS design science, little is known about the impact of design decisions of the cyber part on the overall system qualities. To investigate these interdependencies, this paper proposes a simulation-based Design Space Exploration (DSE) framework that considers detailed cyber system parameters such as cache size, bus width, and voltage levels in addition to physical and control parameters of the CPS. We propose an exploration algorithm that surfs the parameter configurations in the cyber physical sub-systems, in order to approximate the Pareto-optimal design points with regards to the trade-os among the design objectives, such as energy consumption and control stability. We apply the proposed framework to a network control system for an inverted-pendulum application. The presented holistic evaluation of the identified Pareto-points reveals the presence of non-trivial trade-os, which are imposed by the control, physical, and detailed cyber parameters. For instance the identified energy and control optimal design points comprise configurations with a wide range of CPU speeds, sample times and cache configuration following non-trivial zig-zag patterns. The proposed framework could identify and manage those trade-os and, as a result, is an imperative rst step to automate the search for superior CSP configurations