10,996 research outputs found
Requirements for implementing real-time control functional modules on a hierarchical parallel pipelined system
Analysis of a robot control system leads to a broad range of processing requirements. One fundamental requirement of a robot control system is the necessity of a microcomputer system in order to provide sufficient processing capability.The use of multiple processors in a parallel architecture is beneficial for a number of reasons, including better cost performance, modular growth, increased reliability through replication, and flexibility for testing alternate control strategies via different partitioning. A survey of the progression from low level control synchronizing primitives to higher level communication tools is presented. The system communication and control mechanisms of existing robot control systems are compared to the hierarchical control model. The impact of this design methodology on the current robot control systems is explored
Synthesis of multiple-input change asynchronous finite state machines
Asynchronous finite state machines (AFSMS) have been limited because multiple-input changes have been disallowed. In this paper, we present an architecture and synthesis system to overcome this limitation. The AFSM marks potentially hazardous state transitions, and prevents output during them. A synthesis tool to create the AFS M incorporates novel algorithms to detect the hazardous states
Dynamic Control Flow in Large-Scale Machine Learning
Many recent machine learning models rely on fine-grained dynamic control flow
for training and inference. In particular, models based on recurrent neural
networks and on reinforcement learning depend on recurrence relations,
data-dependent conditional execution, and other features that call for dynamic
control flow. These applications benefit from the ability to make rapid
control-flow decisions across a set of computing devices in a distributed
system. For performance, scalability, and expressiveness, a machine learning
system must support dynamic control flow in distributed and heterogeneous
environments.
This paper presents a programming model for distributed machine learning that
supports dynamic control flow. We describe the design of the programming model,
and its implementation in TensorFlow, a distributed machine learning system.
Our approach extends the use of dataflow graphs to represent machine learning
models, offering several distinctive features. First, the branches of
conditionals and bodies of loops can be partitioned across many machines to run
on a set of heterogeneous devices, including CPUs, GPUs, and custom ASICs.
Second, programs written in our model support automatic differentiation and
distributed gradient computations, which are necessary for training machine
learning models that use control flow. Third, our choice of non-strict
semantics enables multiple loop iterations to execute in parallel across
machines, and to overlap compute and I/O operations.
We have done our work in the context of TensorFlow, and it has been used
extensively in research and production. We evaluate it using several real-world
applications, and demonstrate its performance and scalability.Comment: Appeared in EuroSys 2018. 14 pages, 16 figure
Stochastic and adaptive systems : interim report
Includes bibliographical references.Research supported by Air Force Office of Scientific Research (AFSC), Research Grant AFOSR 77-3281. Covers time period, March 1, 1977 to February 28, 1978.by Michael Athans and Sanjoy K. Mitter
Automatic addition of reset in asynchronous sequential control circuits
pre-printAsynchronous finite state machines (AFSMs) usually require initialization to place them in a desired starting state. This normally occurs by toggling a reset signal upon power-up. This paper presents an algorithm to automatically generate power-up reset circuitry thus adding reset to an AFSM after technology mapping. This approach is independent of design methodology since it is applied to a gate netlist. The algorithm ensures all combinational cycles and primary outputs in the circuit are initialized. Options exist in reset generation to minimize the power or performance impact on the AFSM. Results are reported for applying this algorithm to designs of varying size and complexity
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