专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

1. 发明申请

WO2019173075A4 MISSION-CRITICAL AI PROCESSOR WITH MULTI-LAYER FAULT TOLERANCE SUPPORT 审中-公开
公开(公告)号：WO2019173075A4
公开(公告)日：2019-09-12
申请号：PCT/US2019/019451
申请日：2019-02-25
申请人： DINOPLUSAI HOLDINGS LIMITED , CHIN, Chung Kuang , HU, Yujie , WU, Tong , GOLD, Clifford , WONG, Yick Kei , WANG, Xiaosong , SERTILLANGE, Steven , ZHU, Zongwei
发明人： CHIN, Chung Kuang , HU, Yujie , WU, Tong , GOLD, Clifford , WONG, Yick Kei , WANG, Xiaosong , SERTILLANGE, Steven , ZHU, Zongwei
IPC分类号： G06N3/08
摘要： Embodiments described herein provide a mission-critical artificial intelligence (AI) processor (MAIP), which includes multiple types of HEs (hardware elements) comprising one or more HEs configured to perform operations associated with multi-layer NN (neural network) processing, at least one spare HE, a data buffer to store correctly computed data in a previous layer of multi-layer NN processing computed, and fault tolerance (FT) control logic. The FT control logic is configured to: determine a fault in a current layer NN processing associated with the HE; cause the correctly computed data in the previous layer of multi-layer NN processing to be copied or moved to said at least one spare HE; and cause said at least one spare HE to perform the current layer NN processing using said at least one spare HE and the correctly computed data in the previous layer of multi-layer NN processing.

2. 发明申请

WO2019143537A1 MISSION-CRITICAL AI PROCESSOR WITH RECORD AND REPLAY SUPPORT 审中-公开
公开(公告)号：WO2019143537A1
公开(公告)日：2019-07-25
申请号：PCT/US2019/013404
申请日：2019-01-13
申请人： DINOPLUSAI HOLDINGS LIMITED , HU, Yujie , WU, Tong , WANG, Xiaosong , ZHU, Zongwei , CHIN, Chung Kuang , GOLD, Clifford , SERTILLANGE, Steven , WONG, Yick Kei
发明人： HU, Yujie , WU, Tong , WANG, Xiaosong , ZHU, Zongwei , CHIN, Chung Kuang , GOLD, Clifford , SERTILLANGE, Steven , WONG, Yick Kei
IPC分类号： G06F9/38 , G06F13/28 , G06F15/18 , G06N3/02 , G06N99/00
摘要： Embodiments described herein provide a mission-critical artificial intelligence (AI) processor (MAIP), which includes an instruction buffer, processing circuitry, a data buffer, command circuitry, and communication circuitry. During operation, the instruction buffer stores a first hardware instruction and a second hardware instruction. The processing circuitry executes the first hardware instruction, which computes an intermediate stage of an AI model. The data buffer stores data generated from executing the first hardware instruction. The command circuitry determines that the second hardware instruction is a hardware-initiated store instruction for transferring the data from the data buffer. Based on the hardware-initiated store instruction, the communication circuitry transfers the data from the data buffer to a memory device of a computing system, which includes the mission-critical processor, via a communication interface.

3. 发明申请

WO2019173076A1 COMPUTING DEVICE FOR FAST WEIGHTED SUM CALCULATION IN NEURAL NETWORKS 审中-公开
公开(公告)号：WO2019173076A1
公开(公告)日：2019-09-12
申请号：PCT/US2019/019469
申请日：2019-02-25
申请人： DINOPLUSAI HOLDINGS LIMITED , GOLD, Clifford , WU, Tong , HU, Yujie , CHIN, Chung Kuang , WANG, Xiaosong , WONG, Yick Kei
发明人： GOLD, Clifford , WU, Tong , HU, Yujie , CHIN, Chung Kuang , WANG, Xiaosong , WONG, Yick Kei
IPC分类号： G06N3/063 , G06F7/00 , G06F7/499 , G06F7/552 , G06F17/16
摘要： A computing device for fast weighted sum calculation in neural networks is disclosed. The computing device comprises an array of processing elements configured to accept an input array. Each processing element comprises a plurality of multipliers and a multiple levels of accumulators. A set of weights associated with the inputs and a target output are provided to a target processing element to compute the weighted sum for the target output. The device according to the present invention reduces the computation time from M clock cycles to log2M, where M is the size of the input array.

4. 发明申请

WO2019173104A1 AI ACCELERATOR VIRTUALIZATION 审中-公开
公开(公告)号：WO2019173104A1
公开(公告)日：2019-09-12
申请号：PCT/US2019/020074
申请日：2019-02-28
申请人： DINOPLUSAI HOLDINGS LIMITED , HU, Yujie , WANG, Xiaosong , WU, Tong , SERTILLANGE, Steven
发明人： HU, Yujie , WANG, Xiaosong , WU, Tong , SERTILLANGE, Steven
IPC分类号： G06N3/067
摘要： An AI (Artificial Intelligence) processor for Neural Network (NN) Processing shared by multiple users is disclosed. The AI processor comprises a Multiplier Unit (MXU), a Scalar Computing Unit (SCU), a unified buffer coupled to the MXU and SCU to store data and a control circuitry coupled to the CCU and the unified buffer. The MXU comprises a plurality of Processing Elements (PEs) responsible for computing matrix multiplications. The SCU coupled to output of the MXU is responsible for computing the activation function. The control circuitry is configured to perform the space division and time division NN processing for a plurality of users. At one time instance, at least one of the MXU and SCU is shared by two or more users; and at least one user is using a part of the MXU while the other user is using a part of the SCU.

5. 发明申请

WO2017210690A1 SPATIAL AGGREGATION OF HOLISTICALLY-NESTED CONVOLUTIONAL NEURAL NETWORKS FOR AUTOMATED ORGAN LOCALIZATION AND SEGMENTATION IN 3D MEDICAL SCANS 审中-公开
标题翻译：三维医学扫描仪自动化定位及分割的立体卷积神经网络的空间聚合
公开(公告)号：WO2017210690A1
公开(公告)日：2017-12-07
申请号：PCT/US2017/035974
申请日：2017-06-05
申请人： LU, Le , ROTH, Holger , NOGUES, Isabella-Emmanuella , SUMMERS, Ronald , WANG, Xiaosong , HARRISON, Adam P.
发明人： LU, Le , ROTH, Holger , NOGUES, Isabella-Emmanuella , SUMMERS, Ronald , WANG, Xiaosong , HARRISON, Adam P.
IPC分类号： G06K9/00 , G06T7/00
CPC分类号： G06K9/00 , G06K9/6273 , G06N3/0454 , G06N3/084 , G06N5/003 , G06T7/0012 , G06T7/11 , G06T7/143 , G06T2207/10081 , G06T2207/10088 , G06T2207/20072 , G06T2207/20084 , G06T2207/30056 , G06T2207/30092
摘要： Disclosed are systems and methods for localization and segmentation of organs (especially abnormally shaped, deformable, and/or smaller organs, such as the pancreas and lymph nodes) based on data from 3D medical imaging (e.g., CT and MRI scans) using holistically-nested convolutional neural networks ("HNNs"). Using as an example CT scan data and the pancreas, the methods can include localizing an organ from an entire 3D CT scan, providing a reliable bounding box for the more refined segmentation step. The methods can further comprise introducing a fully deep-learning approach, based on an efficient application of HNNs on the three orthogonal views. The resulting HNN per-pixel probability maps can then be fused using pooling to reliably produce a 3D bounding box of the pancreas that maximizes the recall. An introduced localizer compares favorably to both a conventional non-deep-learning method and a hybrid approach based on spatial aggregation of superpixels using random forest classification. The segmentation phase can operate within the computed bounding box and can integrate semantic mid-level cues of deeply-learned organ interior and boundary maps, obtained by two additional and separate realizations pf HNNs. By integrating these two mid-level cues, the disclosed methods are capable of generating boundary-preserving pixel-wise class label maps that result in exceptional final organ segmentations.
摘要翻译：公开了用于基于来自3D医学成像的数据定位和分割器官（特别是异常形状的，可变形的和/或更小的器官，诸如胰腺和淋巴结）的系统和方法（例如， CT和MRI扫描）使用整体嵌套卷积神经网络（“HNN”）。以CT扫描数据和胰腺为例，这些方法可以包括从整个3D CT扫描中定位器官，为更精细的分割步骤提供可靠的边界框。该方法可以进一步包括基于在三个正交视图上有效应用HNN来引入完全深度学习方法。然后可以使用汇集来融合所得到的每个像素的HNN概率图，以可靠地产生胰腺的三维边界框，以最大化召回。引入的定位器与传统的非深度学习方法和基于使用随机森林分类的超级像素的空间聚合的混合方法相比是有利的。分割阶段可以在计算的边界框内运行，并且可以集成深度学习的器官内部和边界图的语义中级线索，通过两个额外的和单独的实现pN HNN获得。通过整合这两个中级线索，所公开的方法能够生成保留边界像素类别标签映射，从而导致异常的最终器官分割。

6. 发明申请

WO2009077531A1 ALTERING HIGH DENSITY LIPOPROTEIN LEVELS THROUGH UDP-N-ACETYL-ALPHA-D-GALACTOSAMINE:POLYPEPTIDE N-ACETLGALACTOSAMINYLTRANSFERASE (GALNT) MODULATION 审中-公开
标题翻译：通过UDP-N-乙酰-ALPHA-D-糖苷酶改变高密度脂蛋白水平：多肽N-乙酰胆碱酯酶基因（GALNT）调节
公开(公告)号：WO2009077531A1
公开(公告)日：2009-06-25
申请号：PCT/EP2008/067636
申请日：2008-12-16
申请人： NOVARTIS AG , THE JACKSON LABORATORY , PAIGEN, Beverly , WANG, Xiaosong
发明人： PAIGEN, Beverly , WANG, Xiaosong
IPC分类号： C12N9/10 , C07K16/40
CPC分类号： C07K16/40 , C12N9/1025
摘要： Described herein are methods for detecting and treating coronary artery disease and atherosclerotic conditions based on modulating the levels of total plasma lipoprotein and HDL-C by inhibiting expression or activity of GALNT. Also described herein are methods for identifying an agent(s) useful in treating coronary artery disease or atherosclerotic conditions.
摘要翻译：本文描述了基于通过抑制GALNT的表达或活性调节总血浆脂蛋白和HDL-C的水平来检测和治疗冠状动脉疾病和动脉粥样硬化病症的方法。本文还描述了用于鉴定用于治疗冠状动脉疾病或动脉粥样硬化病症的药剂的方法。

7. 发明申请

WO2009077479A3 ALTERING HIGH-DENSITY LIPOPROTEIN LEVELS THROUGH CARBOXYLESTERASE 1 MODULATION 审中-公开
公开(公告)号：WO2009077479A3
公开(公告)日：2009-06-25
申请号：PCT/EP2008/067493
申请日：2008-12-15
申请人： NOVARTIS AG , PAIGEN, Beverly , STYLIANO, Ioannis, M. , WANG, Xiaosong
发明人： PAIGEN, Beverly , STYLIANO, Ioannis, M. , WANG, Xiaosong
IPC分类号： A61K39/395 , C07K16/40 , C12N9/18 , C12N15/11 , C12Q1/44
摘要： Described herein are methods for detecting and treating coronary artery disease and atherosclerotic conditions based on modulating the levels of plasma total and HDL cholesterol. Also described herein are methods for identifying an agent(s) useful in treating coronary artery disease or atherosclerotic conditions.

8. 发明申请

WO2009101092A1 FARP2 AND STK25 AND USES THEREOF 审中-公开
标题翻译： FARP2和STK25及其用途
公开(公告)号：WO2009101092A1
公开(公告)日：2009-08-20
申请号：PCT/EP2009/051540
申请日：2009-02-11
申请人： NOVARTIS AG , THE JACKSON LABORATORY , PAIGEN, Beverly , WANG, Xiaosong , SU, Zhiguang
发明人： PAIGEN, Beverly , WANG, Xiaosong , SU, Zhiguang
IPC分类号： C12Q1/68 , A61P9/10
CPC分类号： C12Q1/6883 , C07K16/18 , C12Q2600/118 , C12Q2600/136 , C12Q2600/158 , C12Q2600/172
摘要： Methods and compositions are provided for treating HDL-associated diseases by altering the expression or activity of FARP2 or STK25 or a homolog or ortholog thereof.
摘要翻译：提供了通过改变FARP2或STK25或其同源物或直系同源物的表达或活性来治疗HDL相关疾病的方法和组合物。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式