Efficient Code Generation from SHIM Models

Programming concurrent systems is substantially more difficult than programming sequential systems, yet most embedded systems need concurrency. We believe this should be addressed through higher-level models of concurrency that eliminate many of the usual challenges, such as nondeterminism arising from races. The shim model of computation provides deterministic concurrency, and there already exist ways of implementing it in hardware and software. In this work, we describe how to produce more efficient C code from shim systems. We propose two techniques: a largely mechanical one that produces tail-recursive code for simulating concurrency, and a more clever one that statically analyzes the communication pattern of multiple processes to produce code with far less overhead. Experimentally, we find our tail-recursive technique produces code that runs roughly twice as fast as a baseline; our statically-scheduled code can run up to twelve times faster

Neutronics Studies on the NIST Reactor Using the GA LEU fuel

The National Bureau of Standards Reactor (NBSR) located on the National Institute of Standards and Technology (NIST) Gaithersburg campus, is currently underway of fuel conversion from high enriched uranium (HEU) fuel to low enriched uranium (LEU) fuel. One particular challenging part of the conversion of the NBSR is the high average flux level (2.5×1014 n/cm2-s) required to maintain experimental testing capabilities of the reactor, without significant changes to the external structures of the reactor. Recently the General Atomics (GA) Training Research Isotopes General Atomics (TRIGA) fuel has shown some promising features as a LEU candidate for the high performance research reactors such as the NBSR. The GA fuel has a long history of success in conversion of research reactors since it was developed in 1980s. The UZrH compound in the GA fuel has seen success in long term TRIGA reactors, and is a proven safe LEU alternative. This study performs a neutronics evaluation of the TRIGA fuel under the schema of the NBSR’s heavy conversion requirements in order to examine whether the TRIGA fuel is a viable option for conversion of the NBSR. To determine the most optimal path of conversion, we performed a feasibility study with particular regard to the fuel dimensions, fuel rod configurations, cladding, as well as fuel structure selection. Based on the outcome of the feasibility study, an equilibrium core is then generated following the NBSR’s current fuel management schema. Key neutronics performance characteristics including flux distribution, power distribution, control rod (i.e., shim arms) worth, as well as kinetics parameters of the equilibrium core are calculated and evaluated. MCNP6, a Monte Carlo based computational modeling software was intensively used to aid in these calculations. The results of this study will provide important insight on the effectiveness of conversion, as well as determine the viability of the conversion from HEU to LEU using the GA fuel

A Byzantine Fault-Tolerant Ordering Service for the Hyperledger Fabric Blockchain Platform

Hyperledger Fabric (HLF) is a flexible permissioned blockchain platform designed for business applications beyond the basic digital coin addressed by Bitcoin and other existing networks. A key property of HLF is its extensibility, and in particular the support for multiple ordering services for building the blockchain. Nonetheless, the version 1.0 was launched in early 2017 without an implementation of a Byzantine fault-tolerant (BFT) ordering service. To overcome this limitation, we designed, implemented, and evaluated a BFT ordering service for HLF on top of the BFT-SMaRt state machine replication/consensus library, implementing also optimizations for wide-area deployment. Our results show that HLF with our ordering service can achieve up to ten thousand transactions per second and write a transaction irrevocably in the blockchain in half a second, even with peers spread in different continents

Evolutionary robotics and neuroscience

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Chaotic exploration and learning of locomotion behaviours

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage