MEMTI: optimizing on-chip non-volatile storage for visual multi-task inference at the edge

The combination of specialized hardware and embedded non-volatile memories (eNVM) holds promise for energy-efficient DNN inference at the edge. However, integrating DNN hardware accelerators with eNVMs still presents several challenges. Multi-level programming is desirable for achieving maximal storage density on chip, but the stochastic nature of eNVM writes makes them prone to errors and further increases the write energy and latency. We present MEMTI, a memory architecture that leverages a multi-task learning technique for maximal reuse of DNN parameters across multiple visual tasks. We show that by retraining and updating only 10% of all DNN parameters, we can achieve efficient model adaptation across a variety of visual inference tasks. The system performance is evaluated by integrating the memory with the open-source NVIDIA Deep Learning Architecture (NVDLA)

Automatically accelerating non-numerical programs by architecture-compiler co-design

Because of the high cost of communication between processors, compilers that parallelize loops automatically have been forced to skip a large class of loops that are both critical to performance and rich in latent parallelism. HELIX-RC is a compiler/microprocessor co-design that opens those loops to parallelization by decoupling communication from thread execution in conventional multicore architecures. Simulations of HELIX-RC, applied to a processor with 16 Intel Atom-like cores, show an average of 6.85× performance speedup for six SPEC CINT2000 benchmarks.</jats:p

Revisiting stepwise ocean oxygenation with authigenic barium enrichments in marine mudrocks

There are current debates around the extent of global ocean oxygenation, particularly from the late Neoproterozoic to the early Paleozoic, based on analyses of various geochemical indices. We present a temporal trend in excess barium (Ba_{excess}) contents in marine organic-rich mudrocks (ORMs) to provide an independent constraint on global ocean redox evolution. The absence of remarkable Ba_{excess} enrichments in Precambrian (>ca. 541 Ma) ORMs suggests limited authigenic Ba formation in oxygen- and sulfate-deficient oceans. By contrast, in the Paleozoic, particularly the early Cambrian, ORMs are marked by significant Ba_{excess} enrichments, corresponding to substantial increases in the marine sulfate reservoir and oxygenation level. Analogous to modern sediments, the Mesozoic and Cenozoic ORMs exhibit no prominent Ba_{excess} enrichments. We suggest that variations in Ba_{excess} concentrations of ORMs through time are linked to secular changes in the marine dissolved Ba reservoir associated with elevated marine sulfate levels and global ocean oxygenation. Further, unlike Mo, U, and Re abundances, significant Ba_{excess} enrichments in ORMs indicate that the overall ocean oxygenation level in the early Paleozoic was substantially lower than at present

Chasing Carbon: The Elusive Environmental Footprint of Computing

Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its environmental impact. This paper brings the issue to the attention of computer-systems researchers. Our analysis, built on industry-reported characterization, quantifies the environmental effects of computing in terms of carbon emissions. Broadly, carbon emissions have two sources: operational energy consumption, and hardware manufacturing and infrastructure. Although carbon emissions from the former are decreasing thanks to algorithmic, software, and hardware innovations that boost performance and power efficiency, the overall carbon footprint of computer systems continues to grow. This work quantifies the carbon output of computer systems to show that most emissions related to modern mobile and data-center equipment come from hardware manufacturing and infrastructure. We therefore outline future directions for minimizing the environmental impact of computing systems

A 16-nm SoC for Noise-Robust Speech and NLP Edge AI Inference With Bayesian Sound Source Separation and Attention-Based DNNs

The proliferation of personal artificial intelligence (AI) -assistant technologies with speech-based conversational AI interfaces is driving the exponential growth in the consumer Internet of Things (IoT) market. As these technologies are being applied to keyword spotting (KWS), automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech (TTS) applications, it is of paramount importance that they provide uncompromising performance for context learning in long sequences, which is a key benefit of the attention mechanism, and that they work seamlessly in polyphonic environments. In this work, we present a 25-mm $^2$ system-on-chip (SoC) in 16-nm FinFET technology, codenamed SM6, which executes end-to-end speech-enhancing attention-based ASR and NLP workloads. The SoC includes: 1) FlexASR, a highly reconfigurable NLP inference processor optimized for whole-model acceleration of bidirectional attention-based sequence-to-sequence (seq2seq) deep neural networks (DNNs); 2) a Markov random field source separation engine (MSSE), a probabilistic graphical model accelerator for unsupervised inference via Gibbs sampling, used for sound source separation; 3) a dual-core Arm Cortex A53 CPU cluster, which provides on-demand single Instruction/multiple data (SIMD) fast fourier transform (FFT) processing and performs various application logic (e.g., expectation&#x2013;maximization (EM) algorithm and 8-bit floating-point (FP8) quantization); and 4) an always-on M0 subsystem for audio detection and power management. Measurement results demonstrate the efficiency ranges of 2.6&#x2013;7.8 TFLOPs/W and 4.33&#x2013;17.6 Gsamples/s/W for FlexASR and MSSE, respectively; MSSE denoising performance allowing 6 $\times$ smaller ASR model to be stored on-chip with negligible accuracy loss; and 2.24-mJ energy consumption while achieving real-time throughput, end-to-end, and per-frame ASR latencies of 18 ms

Bridging Python to Silicon: The SODA Toolchain

Systems performing scientific computing, data analysis, and machine learning tasks have a growing demand for application-specific accelerators that can provide high computational performance while meeting strict size and power requirements. However, the algorithms and applications that need to be accelerated are evolving at a rate that is incompatible with manual design processes based on hardware description languages. Agile hardware design tools based on compiler techniques can help by quickly producing an application specific integrated circuit (ASIC) accelerator starting from a high-level algorithmic description. We present the SODA Synthesizer, a modular and open-source hardware compiler that provides automated end-to-end synthesis from high-level software frameworks to ASIC implementation, relying on multi-level representations to progressively lower and optimize the input code. Our approach does not require the application developer to write register-transfer level code, and it is able to reach up to 364 GFLOPS/W efficiency (32-bit precision) on typical convolutional neural network operators

A thermodynamic unification of jamming

Fragile materials ranging from sand to fire-retardant to toothpaste are able to exhibit both solid and fluid-like properties across the jamming transition. Unlike ordinary fusion, systems of grains, foams and colloids jam and cease to flow under conditions that still remain unknown. Here we quantify jamming via a thermodynamic approach by accounting for the structural ageing and the shear-induced compressibility of dry sand. Specifically, the jamming threshold is defined using a non-thermal temperature that measures the 'fluffiness' of a granular mixture. The thermodynamic model, casted in terms of pressure, temperature and free-volume, also successfully predicts the entropic data of five molecular glasses. Notably, the predicted configurational entropy avoids the Kauzmann paradox entirely. Without any free parameters, the proposed equation-of-state also governs the mechanism of shear-banding and the associated features of shear-softening and thickness-invariance.Comment: 16 pgs double spaced. 4 figure

Biphenyls from aerial parts of Ribes takare

Three new biphenyls, 4,7,8-trimethoxy-2,3-methylenedioxydibenzofuran (1), 7-hydroxy-4,8dimethoxy-2,3-methylenedioxydibenzofuran (2), and 3',5-dimethoxy-3,4-methylenedioxybiphenyl (3), along with eighteen known compounds (4-21) were isolated from the aerial part of Ribes takare D. Don. Their structures were elucidated on the basis of spectroscopic data. Compound 1 and compound 2 showed mild alpha-glucosidase inhibitory activity. (C) 2013 Guo-You Li and Dong-Mei Fang. Published by Elsevier B.V. on behalf of Chinese Chemical Society. All rights reserved

Inhibition of PI3K Prevents the Proliferation and Differentiation of Human Lung Fibroblasts into Myofibroblasts: The Role of Class I P110 Isoforms

Idiopathic pulmonary fibrosis (IPF) is a progressive fibroproliferative disease characterized by an accumulation of fibroblasts and myofibroblasts in the alveolar wall. Even though the pathogenesis of this fatal disorder remains unclear, transforming growth factor-β (TGF-β)-induced differentiation and proliferation of myofibroblasts is recognized as a primary event. The molecular pathways involved in TGF-β signalling are generally Smad-dependent yet Smad-independent pathways, including phosphatidylinositol-3-kinase/protein kinase B (PI3K/Akt), have been recently proposed. In this research we established ex-vivo cultures of human lung fibroblasts and we investigated the role of the PI3K/Akt pathway in two critical stages of the fibrotic process induced by TGF-β: fibroblast proliferation and differentiation into myofibroblasts. Here we show that the pan-inhibitor of PI3Ks LY294002 is able to abrogate the TGF-β-induced increase in cell proliferation, in α- smooth muscle actin expression and in collagen production besides inhibiting Akt phosphorylation, thus demonstrating the centrality of the PI3K/Akt pathway in lung fibroblast proliferation and differentiation. Moreover, for the first time we show that PI3K p110δ and p110γ are functionally expressed in human lung fibroblasts, in addition to the ubiquitously expressed p110α and β. Finally, results obtained with both selective inhibitors and gene knocking-down experiments demonstrate a major role of p110γ and p110α in both TGF-β-induced fibroblast proliferation and differentiation. This finding suggests that specific PI3K isoforms can be pharmacological targets in IPF

Sequence variations in DNA repair gene XPC is associated with lung cancer risk in a Chinese population: a case-control study

<p>Abstract</p> <p>Background</p> <p>The nucleotide excision repair (NER) protein, xeroderma pigmentosum C (XPC), participates in recognizing DNA lesions and initiating DNA repair in response to DNA damage. Because mutations in <it>XPC </it>cause a high risk of cancer in XP patients, we hypothesized that inherited sequence variations in <it>XPC </it>may alter DNA repair and thus susceptibility to cancer.</p> <p>Methods</p> <p>In this hospital-based case-control study, we investigated five <it>XPC </it>tagging, common single nucleotide polymorphisms (tagging SNPs) in 1,010 patients with newly diagnosed lung cancer and 1,011 matched cancer free controls in a Chinese population.</p> <p>Results</p> <p>In individual tagging SNP analysis, we found that rs3731055<it>AG+AA </it>variant genotypes were associated with a significantly decreased risk of lung adenocarcinoma [adjusted odds ratio (OR), 0.71; 95% confidence interval (CI), 0.56–0.90] but an increased risk of small cell carcinomas [adjusted OR, 1.79; 95% CI, 1.05–3.07]. Furthermore, we found that haplotype <it>ACCCA </it>was associated with a decreased risk of lung adenocarcinoma [OR, 0.78; 95% CI, 0.62–0.97] but an increased risk of small cell carcinomas [OR, 1.68; 95% CI, 1.04–2.71], which reflected the presence of rs3731055<it>A </it>allele in this haplotype. Further stratified analysis revealed that the protective effect of rs3731055<it>AG+AA </it>on risk of lung adenocarcinoma was more evident among young subjects (age ≤ 60) and never smokers.</p> <p>Conclusion</p> <p>These results suggest that inherited sequence variations in <it>XPC </it>may modulate risk of lung cancer, especially lung adenocarcinoma, in Chinese populations. However, these findings need to be verified in larger confirmatory studies with more comprehensively selected tagging SNPs.</p