Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices

A recent trend in DNN development is to extend the reach of deep learning applications to platforms that are more resource and energy constrained, e.g., mobile devices. These endeavors aim to reduce the DNN model size and improve the hardware processing efficiency, and have resulted in DNNs that are much more compact in their structures and/or have high data sparsity. These compact or sparse models are different from the traditional large ones in that there is much more variation in their layer shapes and sizes, and often require specialized hardware to exploit sparsity for performance improvement. Thus, many DNN accelerators designed for large DNNs do not perform well on these models. In this work, we present Eyeriss v2, a DNN accelerator architecture designed for running compact and sparse DNNs. To deal with the widely varying layer shapes and sizes, it introduces a highly flexible on-chip network, called hierarchical mesh, that can adapt to the different amounts of data reuse and bandwidth requirements of different data types, which improves the utilization of the computation resources. Furthermore, Eyeriss v2 can process sparse data directly in the compressed domain for both weights and activations, and therefore is able to improve both processing speed and energy efficiency with sparse models. Overall, with sparse MobileNet, Eyeriss v2 in a 65nm CMOS process achieves a throughput of 1470.6 inferences/sec and 2560.3 inferences/J at a batch size of 1, which is 12.6x faster and 2.5x more energy efficient than the original Eyeriss running MobileNet. We also present an analysis methodology called Eyexam that provides a systematic way of understanding the performance limits for DNN processors as a function of specific characteristics of the DNN model and accelerator design; it applies these characteristics as sequential steps to increasingly tighten the bound on the performance limits.Comment: accepted for publication in IEEE Journal on Emerging and Selected Topics in Circuits and Systems. This extended version on arXiv also includes Eyexam in the appendi

GPER-induced signaling is essential for the survival of breast cancer stem cells.

G protein-coupled estrogen receptor-1 (GPER), a member of the G protein-coupled receptor (GPCR) superfamily, mediates estrogen-induced proliferation of normal and malignant breast epithelial cells. However, its role in breast cancer stem cells (BCSCs) remains unclear. Here we showed greater expression of GPER in BCSCs than non-BCSCs of three patient-derived xenografts of ER- /PR+ breast cancers. GPER silencing reduced stemness features of BCSCs as reflected by reduced mammosphere forming capacity in vitro, and tumor growth in vivo with decreased BCSC populations. Comparative phosphoproteomics revealed greater GPER-mediated PKA/BAD signaling in BCSCs. Activation of GPER by its ligands, including tamoxifen (TMX), induced phosphorylation of PKA and BAD-Ser118 to sustain BCSC characteristics. Transfection with a dominant-negative mutant BAD (Ser118Ala) led to reduced cell survival. Taken together, GPER and its downstream signaling play a key role in maintaining the stemness of BCSCs, suggesting that GPER is a potential therapeutic target for eradicating BCSCs

Dynamically generated JP=1/2−(3/2−)J^P=1/2^-(3/2^-) singly charmed and bottom heavy baryons

Approximate heavy-quark spin and flavor symmetry and chiral symmetry play an important role in our understanding of the nonperturbative regime of strong interactions. In this work, utilizing the unitarized chiral perturbation theory, we explore the consequences of these symmetries in the description of the interactions between the ground-state singly charmed (bottom) baryons and the pseudo-Nambu-Goldstone bosons. In particular, at leading order in the chiral expansion, by fixing the only parameter in the theory to reproduce the $\Lambda_b(5912)$ [$\Lambda_b^*(5920)$] or the $\Lambda_c(2595)$ [$\Lambda_c^*(2625)$], we predict a number of dynamically generated states, which are contrasted with those of other approaches and available experimental data. In anticipation of future lattice QCD simulations, we calculate the corresponding scattering lengths and compare them to the existing predictions from a $\mathcal{O}(p^3)$ chiral perturbation theory study. In addition, we estimate the effects of the next-to-leading-order potentials by adopting heavy-meson Lagrangians and fixing the relevant low-energy constants using either symmetry or naturalness arguments. It is shown that higher-order potentials play a relatively important role in many channels, indicating that further studies are needed once more experimental or lattice QCD data become available.Comment: 35 pages, 2 figures; extended to 3/2^- sector and the effects of NLO potentials estimated; to appear in Physical Review

Highly efficient coherent optical memory based on electromagnetically induced transparency

Quantum memory is an important component in the long-distance quantum communication system based on the quantum repeater protocol. To outperform the direct transmission of photons with quantum repeaters, it is crucial to develop quantum memories with high fidelity, high efficiency and a long storage time. Here, we achieve a storage efficiency of 92.0(1.5)\% for a coherent optical memory based on the electromagnetically induced transparency (EIT) scheme in optically dense cold atomic media. We also obtain a useful time-bandwidth product of 1200, considering only storage where the retrieval efficiency remains above 50\%. Both are the best record to date in all kinds of the schemes for the realization of optical memory. Our work significantly advances the pursuit of a high-performance optical memory and should have important applications in quantum information science.Comment: 5 pages, 5 figures, supplementary materials: 12 pages, 4 figure

STING-mediated disruption of calcium homeostasis chronically activates ER stress and primes T cell death

STING gain-of-function mutations cause lung disease and T cell cytopenia through unknown mechanisms. Here, we found that these mutants induce chronic activation of ER stress and unfolded protein response (UPR), leading to T cell death by apoptosis in th

Predicting drug response of tumors from integrated genomic profiles by deep neural networks

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However, due to essential differences between cell lines and tumors, the translation into predicting drug response in tumors remains challenging. Here we proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contains a mutation and an expression encoders pre-trained using a large pan-cancer dataset to abstract core representations of high-dimension data, followed by a drug response predictor network. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods and four analog DNNs of our model. We then applied the model to predict drug response of 9,059 tumors of 33 cancer types. The model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Overall, our model and findings improve the prediction of drug response and the identification of novel therapeutic options.Comment: Accepted for presentation in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA. Currently under consideration for publication in a Supplement Issue of BMC Genomic