Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks

Loom (LM), a hardware inference accelerator for Convolutional Neural Networks (CNNs) is presented. In LM every bit of data precision that can be saved translates to proportional performance gains. Specifically, for convolutional layers LM's execution time scales inversely proportionally with the precisions of both weights and activations. For fully-connected layers LM's performance scales inversely proportionally with the precision of the weights. LM targets area- and bandwidth-constrained System-on-a-Chip designs such as those found on mobile devices that cannot afford the multi-megabyte buffers that would be needed to store each layer on-chip. Accordingly, given a data bandwidth budget, LM boosts energy efficiency and performance over an equivalent bit-parallel accelerator. For both weights and activations LM can exploit profile-derived perlayer precisions. However, at runtime LM further trims activation precisions at a much smaller than a layer granularity. Moreover, it can naturally exploit weight precision variability at a smaller granularity than a layer. On average, across several image classification CNNs and for a configuration that can perform the equivalent of 128 16b x 16b multiply-accumulate operations per cycle LM outperforms a state-of-the-art bit-parallel accelerator [1] by 4.38x without any loss in accuracy while being 3.54x more energy efficient. LM can trade-off accuracy for additional improvements in execution performance and energy efficiency and compares favorably to an accelerator that targeted only activation precisions. We also study 2- and 4-bit LM variants and find the the 2-bit per cycle variant is the most energy efficient

INITIAL INSIGHTS INTO THE STRUCTURE-ACTIVITY RELATIONSHIPS OF AVIAN DEFENSINS.

Numerous β-defensins have been identified in birds and the potential use of these peptides as alternatives to antibiotics has been proposed, in particular to fight antibiotic-resistant and zoonotic bacterial species. Little is known about the mechanism of antibacterial activity of avian β-defensins (AvBDs), and the present work was carried out to obtain initial insights into the involvement of structural features or specific residues in the antimicrobial activity of chicken AvBD2. Chicken AvBD2 and its enantiomeric counterpart were chemically synthesized. Peptide elongation and oxidative folding were both optimized. The similar antimicrobial activity measured for both L- and D- proteins clearly indicates that there is no chiral partner. Therefore the bacterial membrane is in all likelihood the primary target. Moreover, this work evidences that the three-dimensional fold is required for an optimal antimicrobial activity, in particular for Gram-positive bacterial strains. The three-dimensional NMR structure of chicken AvBD2 defensin displays the structural 3-stranded antiparallel β-sheet characteristic of β-defensins. The surface of the molecule does not display any amphipathic character. In light of this new structure and of the king penguin AvBD103b defensin structure, the consensus sequence of avian β-defensin's family was analyzed. Well conserved residues were highlighted and the potential strategic role of the lysine 31 residue of AvBD2 emphasized. The synthetic AvBD2-K31A variant displayed substantial N-terminal structural modifications and a dramatic decrease in activity. Taken together, these results demonstrate the structural as well as the functional role of the critical lysine 31 residue in antimicrobial activity

YY1 Regulates Melanocyte Development and Function by Cooperating with MITF

Studies of coat color mutants have greatly contributed to the discovery of genes that regulate melanocyte development and function. Here, we generated Yy1 conditional knockout mice in the melanocyte-lineage and observed profound melanocyte deficiency and premature gray hair, similar to the loss of melanocytes in human piebaldism and Waardenburg syndrome. Although YY1 is a ubiquitous transcription factor, YY1 interacts with M-MITF, the Waardenburg Syndrome IIA gene and a master transcriptional regulator of melanocytes. YY1 cooperates with M-MITF in regulating the expression of piebaldism gene KIT and multiple additional pigmentation genes. Moreover, ChIP–seq identified genome-wide YY1 targets in the melanocyte lineage. These studies mechanistically link genes implicated in human conditions of melanocyte deficiency and reveal how a ubiquitous factor (YY1) gains lineage-specific functions by co-regulating gene expression with a lineage-restricted factor (M-MITF)—a general mechanism which may confer tissue-specific gene expression in multiple lineages

Determinants of Nigerian managers’ environmental attitude: Africa's Ubuntu ethics versus global capitalism

We investigate the impact of economic, institutional, and ethical pressures on African managers' corporate social and environmental attitude based on a survey involving 377 Nigerian executives in the extractive industry. We find that environmental orientation and behavior are mostly induced by instrumental economic motives, while ethical considerations exert a weak impact. This finding is significant because it contradicts mainstream corporate social responsibility (CSR) literature in Africa, which suggests the dominance of the culturally based, altruistic African Ubuntu philosophy. Based on this research finding, we suggest that economic globalization has spurned a transnational capitalist cadre of managers whose values are shaped far more by global capitalist instincts than any putative cultural philosophy. The findings also undercut the fundamental logic underpinning the numerous global initiatives to promote environmental responsibility by multinational corporations in developing countries, which assumes that managers will pursue environmental sustainability voluntarily in the absence of robust regulations and strict enforcement

Linked Data and Open Data in Cultural Heritage - from panel 'Linked Art: Networking Digital Collections and Scholarship'

Cultural heritage institutions have a great deal to gain from deeply engaging in the networked environment. They have poured many resources in the digitization of their collections for the benefit of their audiences, from students to experts, who want to have access to more online material of a higher quality. The current landscape of cultural heritage knowledge on the Web, however, is very much siloed, which both harms the relevance of individual institutions as well as the overall state of scholarship which might use that knowledge. This talk will give an overview of previous initiatives and technologies intended to open up access to cultural heritage institutions, particularly art museums, including the International Council of Museums Committee for Documentation Conceptual Reference Model (CIDOC CRM, also an ISO standard); The American Art Collaborative (AAC); PHAROS, the International Consortium of Photo Archives; the Art and Architecture Thesaurus (AAT); the Union List of Artist Names (ULAN); and the International Image Interoperability Framework (IIIF). In the context of the successes and limitations of these efforts, this paper will outline the strategy taken by Linked Art. This presentation will challenge the traditional paradigm which has large and wealthy institutions succeed in the face of structural challenges. In this era of hyper connectedness, the solution to museums’ relevance in the Web cannot be developed by a lone institution, and indeed the model that Linked Art promotes is instead based on inclusion with the goal to create institutional and individual partnerships. The other precept that the Linked Art data model advocates for is usability over absolute data completeness, and this talk will go over some specific data modeling principles that allow balance between the requirements of the institution, domain knowledge experts, technologists who will implement the standard, and scholars and other users of Linked Art

Certifying Emergency Landing for Safe Urban UAV

International audienceUnmanned Aerial Vehicles (UAVs) have the potential to be used for many applications in urban environments. However, allowing UAVs to fly above densely populated areas raises concerns regarding safety. One of the main safety issues is the possibility for a failure to cause the loss of navigation capabilities, which can result in the UAV falling/landing in hazardous areas such as busy roads, where it can cause fatal accidents. Current standards, such as the SORA published in 2019, do not consider applicable mitigation techniques to handle this kind of hazardous situations. Consequently, certifying UAV urban operations implies to demonstrate very high levels of integrity, which results in prohibitive development costs. To address this issue, this paper explores the concept of Emergency Landing (EL). A safety analysis is conducted on an urban UAV case study, and requirements are proposed to enable the integration of EL as an acceptable mitigation mean in the SORA. Based on these requirements, an EL implementation was developed, together with a runtime monitoring architecture to enhance confidence in the system. Preliminary qualitative results are presented and the monitor seem to be able to detect errors of the EL system effectively

Evaluation of Runtime Monitoring for UAV Emergency Landing

International audienceTo certify UAV operations in populated areas, risk mitigation strategies-such as Emergency Landing (EL)must be in place to account for potential failures. EL aims at reducing ground risk by finding safe landing areas using onboard sensors. The first contribution of this paper is to present a new EL approach, in line with safety requirements introduced in recent research. In particular, the proposed EL pipeline includes mechanisms to monitor learning based components during execution. This way, another contribution is to study the behavior of Machine Learning Runtime Monitoring (MLRM) approaches within the context of a real-world critical system. A new evaluation methodology is introduced, and applied to assess the practical safety benefits of three MLRM mechanisms. The proposed approach is compared to a default mitigation strategy (open a parachute when a failure is detected), and appears to be much safer

Evaluation of Runtime Monitoring for UAV Emergency Landing

International audienceTo certify UAV operations in populated areas, risk mitigation strategies-such as Emergency Landing (EL)must be in place to account for potential failures. EL aims at reducing ground risk by finding safe landing areas using onboard sensors. The first contribution of this paper is to present a new EL approach, in line with safety requirements introduced in recent research. In particular, the proposed EL pipeline includes mechanisms to monitor learning based components during execution. This way, another contribution is to study the behavior of Machine Learning Runtime Monitoring (MLRM) approaches within the context of a real-world critical system. A new evaluation methodology is introduced, and applied to assess the practical safety benefits of three MLRM mechanisms. The proposed approach is compared to a default mitigation strategy (open a parachute when a failure is detected), and appears to be much safer