Generation of Efficient High-Level Hardware Code from Dataflow Programs

High-level synthesis (HLS) aims at reducing the time-to-market by providing an automated design process that interprets and compiles high-level abstraction programs into hardware. However, HLS tools still face limitations regarding the performance of the generated code, due to the difficulties of compiling input imperative languages into efficient hardware code. Moreover the hardware code generated by the HLS tools is usually target-dependant and at a low level of abstraction (i.e. gate-level). A generated code at a high-level of abstraction (i.e. chip-level) is better suited to the needs of systems' architects because they can understand and control all of the design processes. We propose in this paper a new approach to HLS to generate efficient, high-level hardware code from Dataflow Programs. Implementation results (from two dynamic dataflow programs) on Xilinx, Altera and Latice FPGAs and on ASIC targeting 90nm CMOS technology are also presented

Robust Temporal Logic Model Predictive Control

Control synthesis from temporal logic specifications has gained popularity in recent years. In this paper, we use a model predictive approach to control discrete time linear systems with additive bounded disturbances subject to constraints given as formulas of signal temporal logic (STL). We introduce a (conservative) computationally efficient framework to synthesize control strategies based on mixed integer programs. The designed controllers satisfy the temporal logic requirements, are robust to all possible realizations of the disturbances, and optimal with respect to a cost function. In case the temporal logic constraint is infeasible, the controller satisfies a relaxed, minimally violating constraint. An illustrative case study is included.Comment: This work has been accepted to appear in the proceedings of 53rd Annual Allerton Conference on Communication, Control and Computing, Urbana-Champaign, IL (2015

Leveraging Language to Learn Program Abstractions and Search Heuristics

Inductive program synthesis, or inferring programs from examples of desired behavior, offers a general paradigm for building interpretable, robust, and generalizable machine learning systems. Effective program synthesis depends on two key ingredients: a strong library of functions from which to build programs, and an efficient search strategy for finding programs that solve a given task. We introduce LAPS (Language for Abstraction and Program Search), a technique for using natural language annotations to guide joint learning of libraries and neurally-guided search models for synthesis. When integrated into a state-of-the-art library learning system (DreamCoder), LAPS produces higher-quality libraries and improves search efficiency and generalization on three domains -- string editing, image composition, and abstract reasoning about scenes -- even when no natural language hints are available at test time.Comment: appeared in Thirty-eighth International Conference on Machine Learning (ICML 2021

Sampling for Bayesian program learning

Towards learning programs from data, we introduce the problem of sampling programs from posterior distributions conditioned on that data. Within this setting, we propose an algorithm that uses a symbolic solver to efficiently sample programs. The proposal combines constraint-based program synthesis with sampling via random parity constraints. We give theoretical guarantees on how well the samples approximate the true posterior, and have empirical results showing the algorithm is efficient in practice, evaluating our approach on 22 program learning problems in the domains of text editing and computer-aided programming.National Science Foundation (U.S.) (Award NSF-1161775)United States. Air Force Office of Scientific Research (Award FA9550-16-1-0012

Periodization Programs and their Effects on the Physiological Outcomes of Collegiate Athletes

Many different strength and conditioning professionals are attempting to find more efficient ways to train their athletes to improve strength, power, body mass and body composition. There are many different types of training models that are used within the realm of strength and conditioning. Therefore, the purpose of this synthesis was to review the literature on periodization programs and their effects of physiological outcomes on collegiate athletes. Research has shown that both Linear and Nonlinear periodization models improved physiological outcomes of the subjects presented. With that being said, there was no sufficient evidence to which model is more efficient. Further education and studies need to be conducted for future researc

Program Synthesis With Types

Program synthesis, the automatic generation of programs from specification, promises to fundamentally change the way that we build software. By using synthesis tools, we can greatly speed up the time it takes to build complex software artifacts as well as construct programs that are automatically correct by virtue of the synthesis process. Studied since the 70s, researchers have applied techniques from many different sub-fields of computer science to solve the program synthesis problem in a variety of domains and contexts. However, one domain that has been less explored than others is the domain of typed, functional programs. This is unfortunate because programs in richly-typed languages like OCaml and Haskell are known for ``writing themselves\u27\u27 once the programmer gets the types correct. In light of this observation, can we use type theory to build more expressive and efficient type-directed synthesis systems for this domain of programs? This dissertation answers this question in the affirmative by building novel type-theoretic foundations for program synthesis. By using type theory as the basis of study for program synthesis, we are able to build core synthesis calculi for typed, functional programs, analyze the calculi\u27s meta-theoretic properties, and extend these calculi to handle increasingly richer types and language features. In addition to these foundations, we also present an implementation of these synthesis systems, Myth, that demonstrates the effectiveness of program synthesis with types on real-world code