Octopus: A Heterogeneous In-network Computing Accelerator Enabling Deep Learning for network

Deep learning (DL) for network models have achieved excellent performance in the field and are becoming a promising component in future intelligent network system. Programmable in-network computing device has great potential to deploy DL for network models, however, existing device cannot afford to run a DL model. The main challenges of data-plane supporting DL-based network models lie in computing power, task granularity, model generality and feature extracting. To address above problems, we propose Octopus: a heterogeneous in-network computing accelerator enabling DL for network models. A feature extractor is designed for fast and efficient feature extracting. Vector accelerator and systolic array work in a heterogeneous collaborative way, offering low-latency-highthroughput general computing ability for packet-and-flow-based tasks. Octopus also contains on-chip memory fabric for storage and connecting, and Risc-V core for global controlling. The proposed Octopus accelerator design is implemented on FPGA. Functionality and performance of Octopus are validated in several use-cases, achieving performance of 31Mpkt/s feature extracting, 207ns packet-based computing latency, and 90kflow/s flow-based computing throughput

Circumstantial evidence for a soft nuclear symmetry energy at supra-saturation densities

Within an isospin- and momentum-dependent hadronic transport model it is shown that the recent FOPI data on the $\pi^-/\pi^+$ ratio in central heavy-ion collisions at SIS/GSI energies (Willy Reisdorf {\it et al.}, NPA {\bf 781}, 459 (2007)) provide circumstantial evidence suggesting a rather soft nuclear symmetry energy \esym at $\rho\geq 2\rho_0$ compared to the Akmal-Pandharipande-Ravenhall prediction. Some astrophysical implications and the need for further experimental confirmations are discussed.Comment: Version to appear in Phys. Rev. Let

Quantum spin Hall effect and spin-charge separation in a kagome lattice

A two-dimensional kagome lattice is theoretically investigated within a simple tight-binding model, which includes the nearest neighbor hopping term and the intrinsic spin-orbit interaction between the next nearest neighbors. By using the topological winding properties of the spin-edge states on the complex-energy Riemann surface, the spin Hall conductance is obtained to be quantized as $-e/2\pi$ ($e/2\pi$) in insulating phases. This result keeps consistent with the numerical linear-response calculation and the \textbf{Z}$_{2}$ topological invariance analysis. When the sample boundaries are connected in twist, by which two defects with $\pi$ flux are introduced, we obtain the spin-charge separated solitons at 1/3 (or 2/3) filling.Comment: 13 NJP pages, 7 figure

Lag synchronization of switched neural networks via neural activation function and applications in image encryption

This paper investigates the problem of global exponential lag synchronization of a class of switched neural networks with time-varying delays via neural activation function and applications in image encryption. The controller is dependent on the output of the system in the case of packed circuits, since it is hard to measure the inner state of the circuits. Thus, it is critical to design the controller based on the neuron activation function. Comparing the results, in this paper, with the existing ones shows that we improve and generalize the results derived in the previous literature. Several examples are also given to illustrate the effectiveness and potential applications in image encryption