A Behavioral Design Flow for Synthesis and Optimization of Asynchronous Systems

Asynchronous or clockless design is believed to hold the promise of alleviating many of the challenges currently facing microelectronic design. Distributing a high-speed clock signal across an entire chip is an increasing challenge, particularly as the number of transistors on chip continues to rise. With increasing heterogeneity in massively multi- core processors, the top-level system integration is already elastic in nature. Future computing technologies (e.g., nano, quantum, etc.) are expected to have unpredictable timing as well. Therefore, asynchronous design techniques are gaining relevance in mainstream design. Unfortunately, the field of asynchronous design lacks mature design tools for creating large-scale, high-performance or energy-efficient systems. This thesis attempts to fill the void by contributing a set of design methods and automated tools for synthesizing asynchronous systems from high-level specifications. In particular, this thesis provides methods and tools for: (i) generating high-speed pipelined implementations from behavioral specifications, (ii) sharing and scheduling resources to conserve area while providing high performance, and (iii) incorporating energy and power considerations into high-level design. These methods are incorporated into a comprehensive design flow that provides a choice of synthesis paths to the designer, and a mechanism to explore the spectrum between them. The first path specifically targets the highest-performance implementations using data-driven pipelined circuits. The second path provides an alternative approach that targets low-area implementations, providing for optimal resource sharing and optimal scheduling techniques to achieve performance targets. Finally, the third path through the design flow allows the entire spectrum between the two extremes to be explored. In particular, it is a hybrid approach that preserves a pipelined architecture but still allows sharing of resources. By varying performance targets, a wide range of designs can be realized. A variety of metrics are incorporated as constraints or cost functions: area, latency, cycle time, energy consumption, and peak power. Experimental results demonstrate the capability of the proposed design flow to quickly produce optimized specifications. By automating synthesis and optimization, this thesis shows that the designer effort necessary to produce a high-quality solution can be significantly reduced. It is hoped that this work provides a path towards more mature automation and design tools for asynchronous design

Constraint Programming for Scheduling

Our goal is to introduce the constraint programming (CP) approach within the context of scheduling. We start with an introduction to CP and its distinct technical vocabulary. We then present and illustrate a general algorithm for solving a CP problem with a simple scheduling example. Next, we review several published studies where CP has been used in scheduling problems so as to provide a feel for its applicability. We discuss the advantages of CP in modeling and solving certain types of scheduling problems. We then provide an illustration of the use of a commercial CP tool (OPL Studio) in modeling and designing a solution procedure for a classic problem in scheduling. We conclude with our speculations about the future of scheduling research using this approach

Running deep learning applications on resource constrained devices

The high accuracy of Deep Neural Networks (DNN) come at the expense of high computational cost and memory requirements. During inference, the data is often collected on the edge device which are resource-constrained. The existing solutions for edge deployment include i) executing the entire DNN on the edge (EDGE-ONLY), ii) sending the input from edge to cloud where the DNN is processed (CLOUD-ONLY), and iii) splitting the DNN to execute partially on the edge and partially on the cloud (SPLIT). The choice of deployment between EDGE-ONLY, CLOUD-ONLY and SPLIT is determined by several operating constraints such as device resources and network speed, and application constraints such as latency and accuracy. The EDGE-ONLY approach requires compact DNN with low compute and memory requirements. Thus, the emerging class of DNNs employ low-rank convolutions (LRCONVs) which reduce one or more dimensions compared to the spatial convolutions (CONV). Prior research in hardware accelerators has largely focused on CONVs. The LRCONVs such as depthwise and pointwise convolutions exhibit lower arithmetic intensity and lower data reuse. Thus, LRCONVs result in low hardware utilization and high latency. In our first work, we systematically explore the design space of Cross-layer dataflows to exploit data reuse across layers for emerging DNNs in EDGE-ONLY scenarios. We develop novel fine-grain cross-layer dataflows for LRCONVs that support partial loop dimension completion. Our tool, X-Layer decouples the nested loops in a pipeline and combines them to create a common outer dataflow and several inner dataflows. The CLOUD-ONLY approach can suffer from high latency due to the high transmission cost of large input data from the edge to the cloud. This could be a problem, especially for latency-critical applications. Thankfully, the SPLIT approach reduces latency compared to the CLOUD-ONLY approach. However, existing solutions only split the DNN in floating-point precision. Executing floating-point precision on the edge device can occupy large memory and reduce the potential options for SPLIT solutions. In our second work, we expand and explore the search space of SPLIT solutions by jointly applying mixed-precision post-training quantization and DNN graph split. Our work, Auto-Split finds a balance in the trade-off among the model accuracy, edge device capacity, transmission cost, and the overall latency

Proactive Algorithms for Job Shop Scheduling with Probabilistic Durations

Most classical scheduling formulations assume a fixed and known duration for each activity. In this paper, we weaken this assumption, requiring instead that each duration can be represented by an independent random variable with a known mean and variance. The best solutions are ones which have a high probability of achieving a good makespan. We first create a theoretical framework, formally showing how Monte Carlo simulation can be combined with deterministic scheduling algorithms to solve this problem. We propose an associated deterministic scheduling problem whose solution is proved, under certain conditions, to be a lower bound for the probabilistic problem. We then propose and investigate a number of techniques for solving such problems based on combinations of Monte Carlo simulation, solutions to the associated deterministic problem, and either constraint programming or tabu search. Our empirical results demonstrate that a combination of the use of the associated deterministic problem and Monte Carlo simulation results in algorithms that scale best both in terms of problem size and uncertainty. Further experiments point to the correlation between the quality of the deterministic solution and the quality of the probabilistic solution as a major factor responsible for this success