A Unified Component Modeling Approach for Performance Estimation in Hardware/Software Codesign

This paper presents an approach for abstract modeling of hardware/software architectures using Hierarchical Colored Petri Nets. The approach is able to capture complex behavioral characteristics often seen in software and hardware architectures, thus it is suitable for high level codesign issues such as performance estimation. In this paper, the development of a model of the ARM7 processor [5] is described to illustrate the full potential of the modeling approach. To further illustrate the approach, a cache model is also described. The approach and related tools are currently being implemented in the LYCOS system [12]. Details and the basic characteristics of the approach can be found in [8]

Analytic and Bootstrap-after-Cross-Validation Methods for Selecting Penalty Parameters of High-Dimensional M-Estimators

We develop two new methods for selecting the penalty parameter for the $\ell^1$-penalized high-dimensional M-estimator, which we refer to as the analytic and bootstrap-after-cross-validation methods. For both methods, we derive nonasymptotic error bounds for the corresponding $\ell^1$-penalized M-estimator and show that the bounds converge to zero under mild conditions, thus providing a theoretical justification for these methods. We demonstrate via simulations that the finite-sample performance of our methods is much better than that of previously available and theoretically justified methods.Comment: 63 pages, 6 figure

Learning and Generalizing Polynomials in Simulation Metamodeling

The ability to learn polynomials and generalize out-of-distribution is essential for simulation metamodels in many disciplines of engineering, where the time step updates are described by polynomials. While feed forward neural networks can fit any function, they cannot generalize out-of-distribution for higher-order polynomials. Therefore, this paper collects and proposes multiplicative neural network (MNN) architectures that are used as recursive building blocks for approximating higher-order polynomials. Our experiments show that MNNs are better than baseline models at generalizing, and their performance in validation is true to their performance in out-of-distribution tests. In addition to MNN architectures, a simulation metamodeling approach is proposed for simulations with polynomial time step updates. For these simulations, simulating a time interval can be performed in fewer steps by increasing the step size, which entails approximating higher-order polynomials. While our approach is compatible with any simulation with polynomial time step updates, a demonstration is shown for an epidemiology simulation model, which also shows the inductive bias in MNNs for learning and generalizing higher-order polynomials

Hardware Resource Allocation for Hardware/Software Partitioning in the LYCOS System

This paper presents a novel hardware resource alloca-tion technique for hardware/software partitioning. It al-locates hardware resources to the hardware data-path us-ing information such as data-dependencies between op-erations in the application, and profiling information. The algorithm is useful as a designer’s/designtool’s aid to generate good hardware allocations for use in hard-ware/software partitioning. The algorithm has been imple-mented in a tool under the LYCOS system [9]. The results show that the allocations produced by the algorithm come close to the best allocations obtained by exhaustive search.