KRATOS: An Open Source Hardware-Software Platform for Rapid Research in LPWANs

Long-range (LoRa) radio technologies have recently gained momentum in the IoT landscape, allowing low-power communications over distances up to several kilometers. As a result, more and more LoRa networks are being deployed. However, commercially available LoRa devices are expensive and propriety, creating a barrier to entry and possibly slowing down developments and deployments of novel applications. Using open-source hardware and software platforms would allow more developers to test and build intelligent devices resulting in a better overall development ecosystem, lower barriers to entry, and rapid growth in the number of IoT applications. Toward this goal, this paper presents the design, implementation, and evaluation of KRATOS, a low-cost LoRa platform running ContikiOS. Both, our hardware and software designs are released as an open- source to the research community.Comment: Accepted at WiMob 201

Extended Bit-Plane Compression for Convolutional Neural Network Accelerators

After the tremendous success of convolutional neural networks in image classification, object detection, speech recognition, etc., there is now rising demand for deployment of these compute-intensive ML models on tightly power constrained embedded and mobile systems at low cost as well as for pushing the throughput in data centers. This has triggered a wave of research towards specialized hardware accelerators. Their performance is often constrained by I/O bandwidth and the energy consumption is dominated by I/O transfers to off-chip memory. We introduce and evaluate a novel, hardware-friendly compression scheme for the feature maps present within convolutional neural networks. We show that an average compression ratio of 4.4 7 relative to uncompressed data and a gain of 60% over existing method can be achieved for ResNet-34 with a compression block requiring <300 bit of sequential cells and minimal combinational logic

The Cost of Application-Class Processing: Energy and Performance Analysis of a Linux-Ready 1.7-GHz 64-Bit RISC-V Core in 22-nm FDSOI Technology

The open-source RISC-V instruction set architecture (ISA) is gaining traction, both in industry and academia. The ISA is designed to scale from microcontrollers to server-class processors. Furthermore, openness promotes the availability of various open-source and commercial implementations. Our main contribution in this paper is a thorough power, performance, and efficiency analysis of the RISC-V ISA targeting baseline "application class" functionality, i.e., supporting the Linux OS and its application environment based on our open-source single-issue in-order implementation of the 64-bit ISA variant (RV64GC) called Ariane. Our analysis is based on a detailed power and efficiency analysis of the RISC-V ISA extracted from silicon measurements and calibrated simulation of an Ariane instance (RV64IMC) taped-out in GlobalFoundries 22FDX technology. Ariane runs at up to 1.7-GHz, achieves up to 40-Gop/sW energy efficiency, which is superior to similar cores presented in the literature. We provide insight into the interplay between functionality required for the application-class execution (e.g., virtual memory, caches, and multiple modes of privileged operation) and energy cost. We also compare Ariane with RISCY, a simpler and a slower microcontroller-class core. Our analysis confirms that supporting application-class execution implies a nonnegligible energy-efficiency loss and that compute performance is more cost-effectively boosted by instruction extensions (e.g., packed SIMD) rather than the high-frequency operation

Reducing Interconnect Cost in NoC through Serialized Asynchronous Links

This work investigates the application of serialization as a means of reducing the number of wires in NoC combined with asynchronous links in order to simplify the clocking of the link. Throughput is reduced but savings in routing area and reduction in power could make this attractiv

EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators

In the wake of the success of convolutional neural networks in image classification, object recognition, speech recognition, etc., the demand for deploying these compute-intensive ML models on embedded and mobile systems with tight power and energy constraints at low cost, as well as for boosting throughput in data centers, is growing rapidly. This has sparked a surge of research into specialized hardware accelerators. Their performance is typically limited by I/O bandwidth, power consumption is dominated by I/O transfers to off-chip memory, and on-chip memories occupy a large part of the silicon area. We introduce and evaluate a novel, hardware-friendly, and lossless compression scheme for the feature maps present within convolutional neural networks. We present hardware architectures and synthesis results for the compressor and decompressor in 65 nm. With a throughput of one 8-bit word/cycle at 600 MHz, they fit into 2.8 kGE and 3.0 kGE of silicon area, respectively - together the size of less than seven 8-bit multiply-add units at the same throughput. We show that an average compression ratio of 5.1 7 for AlexNet, 4 for VGG-16, 2.4 7 for ResNet-34 and 2.2 7 for MobileNetV2 can be achieved - a gain of 45-70% over existing methods. Our approach also works effectively for various number formats, has a low frame-to-frame variance on the compression ratio, and achieves compression factors for gradient map compression during training that are even better than for inference

New results on path-decompositions and their down-links

In a recent paper the concept of \emph{down-link} from a $(K_v,\Gamma)$-design \cB to a $(K_n,\Gamma')$-design \cB' has been introduced. In the present paper the spectrum problems for $\Gamma'=P_4$ are studied. General results on the existence of path-decompositions and embeddings between path-decompositions playing a fundamental role for the construction of down-links are also presented

Interactive Video Mashup Based on Emotional Identity

The growth of new multimedia technologies has provided the user with the ability to become a videomaker, instead of being merely part of a passive audience. In such a scenario, a new generation of audiovisual content, referred to as video mashup, is gaining consideration and popularity. A mashup is created by editing and remixing pre-existing material to obtain a product which has its own identity and, in some cases, an artistic value itself. In this work we propose an emotional-driven interactive framework for the creation of video mashup. Given a set of feature movies as primary material, during the mixing task the user is supported by a selection of sequences belonging to different movies which share a similar emotional identity, defined through the investigation of cinematographic techniques used by directors to convey emotions

Radiative observables for linearized gravity on asymptotically flat spacetimes and their boundary induced states

We discuss the quantization of linearized gravity on globally hyperbolic, asymptotically flat, vacuum spacetimes and the construction of distinguished states which are both of Hadamard form and invariant under the action of all bulk isometries. The procedure, we follow, consists of looking for a realization of the observables of the theory as a sub-algebra of an auxiliary, non-dynamical algebra constructed on future null infinity $\Im^+$. The applicability of this scheme is tantamount to proving that a solution of the equations of motion for linearized gravity can be extended smoothly to $\Im^+$. This has been claimed to be possible provided that a suitable gauge fixing condition, first written by Geroch and Xanthopoulos, is imposed. We review its definition critically showing that there exists a previously unnoticed obstruction in its implementation leading us to introducing the concept of radiative observables. These constitute an algebra for which a Hadamard state induced from null infinity and invariant under the action of all spacetime isometries exists and it is explicitly constructed.Comment: 31 pages, added reference

A Connotative Space for Supporting Movie Affective Recommendation

The problem of relating media content to users’affective responses is here addressed. Previous work suggests that a direct mapping of audio-visual properties into emotion categories elicited by films is rather difficult, due to the high variability of individual reactions. To reduce the gap between the objective level of video features and the subjective sphere of emotions, we propose to shift the representation towards the connotative properties of movies, in a space inter-subjectively shared among users. Consequently, the connotative space allows to define, relate and compare affective descriptions of film videos on equal footing. An extensive test involving a significant number of users watching famous movie scenes, suggests that the connotative space can be related to affective categories of a single user. We apply this finding to reach high performance in meeting user’s emotional preferences