40,600 research outputs found
A GPU-based hyperbolic SVD algorithm
A one-sided Jacobi hyperbolic singular value decomposition (HSVD) algorithm,
using a massively parallel graphics processing unit (GPU), is developed. The
algorithm also serves as the final stage of solving a symmetric indefinite
eigenvalue problem. Numerical testing demonstrates the gains in speed and
accuracy over sequential and MPI-parallelized variants of similar Jacobi-type
HSVD algorithms. Finally, possibilities of hybrid CPU--GPU parallelism are
discussed.Comment: Accepted for publication in BIT Numerical Mathematic
Designing a CPU model: from a pseudo-formal document to fast code
For validating low level embedded software, engineers use simulators that
take the real binary as input. Like the real hardware, these full-system
simulators are organized as a set of components. The main component is the CPU
simulator (ISS), because it is the usual bottleneck for the simulation speed,
and its development is a long and repetitive task. Previous work showed that an
ISS can be generated from an Architecture Description Language (ADL). In the
work reported in this paper, we generate a CPU simulator directly from the
pseudo-formal descriptions of the reference manual. For each instruction, we
extract the information describing its behavior, its binary encoding, and its
assembly syntax. Next, after automatically applying many optimizations on the
extracted information, we generate a SystemC/TLM ISS. We also generate tests
for the decoder and a formal specification in Coq. Experiments show that the
generated ISS is as fast and stable as our previous hand-written ISS.Comment: 3rd Workshop on: Rapid Simulation and Performance Evaluation: Methods
and Tools (2011
Symbol-Based Successive Cancellation List Decoder for Polar Codes
Polar codes is promising because they can provably achieve the channel
capacity while having an explicit construction method. Lots of work have been
done for the bit-based decoding algorithm for polar codes. In this paper,
generalized symbol-based successive cancellation (SC) and SC list decoding
algorithms are discussed. A symbol-based recursive channel combination
relationship is proposed to calculate the symbol-based channel transition
probability. This proposed method needs less additions than the
maximum-likelihood decoder used by the existing symbol-based polar decoding
algorithm. In addition, a two-stage list pruning network is proposed to
simplify the list pruning network for the symbol-based SC list decoding
algorithm.Comment: Accepted by 2014 IEEE Workshop on Signal Processing Systems (SiPS
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
In this work, we propose a novel robot learning framework called Neural Task
Programming (NTP), which bridges the idea of few-shot learning from
demonstration and neural program induction. NTP takes as input a task
specification (e.g., video demonstration of a task) and recursively decomposes
it into finer sub-task specifications. These specifications are fed to a
hierarchical neural program, where bottom-level programs are callable
subroutines that interact with the environment. We validate our method in three
robot manipulation tasks. NTP achieves strong generalization across sequential
tasks that exhibit hierarchal and compositional structures. The experimental
results show that NTP learns to generalize well to- wards unseen tasks with
increasing lengths, variable topologies, and changing objectives.Comment: ICRA 201
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