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1、无无忧忧PPTPPT整理整理发发布布拥有生物激励神经感知引擎拥有生物激励神经感知引擎 的实时多目标识别处理器的实时多目标识别处理器无无忧忧PPTPPT整理整理发发布布object recognitionFirst, various scale spaces are generated by a cascaded filtering for input video stream.Then, key-points are extracted among neighbor scale spaces by local maxima/minima search, and each of them is
2、converted to a descriptor vector that describes the magnitude and orientation of it.Last, the final recognition is made by nearest neighbor matching with pre-defined object database that generally includes over ten thousands of object descriptor vectors.无无忧忧PPTPPT整理整理发发布布Disadvantage of SIMD process
3、orstheir identical operations are not suitable for key-point or object level operations such as descriptor vector generation and database matching.the multi-core processor of exploits coarse-grained PEs and memory-centric network-on-chip (NoC) for task-level parallelism over data-level parallelism c
4、annot provide enough computing power for real-time object recognition due to its data synchronization overhead.无无忧忧PPTPPT整理整理发发布布The papers workSection II describes a visual perception based multi-object recognition algorithm in detail.Section III explains system architecture of the proposed process
5、or.Detailed designs of each building block are explained in Section IV.Section V describes the architecture. proposed NoC communication The chip implementation and evaluation results follow in Section VI.无无忧忧PPTPPT整理整理发发布布II .visual perception based multi-object recognition A. Visual Perception Base
6、d Object Recognition Model无无忧忧PPTPPT整理整理发发布布B. Overall Algorithm(注：(ROIs)regions-of-interest)无无忧忧PPTPPT整理整理发发布布III. system architecture 无无忧忧PPTPPT整理整理发发布布无无忧忧PPTPPT整理整理发发布布IV. building block designA. Neural Perception Engine无无忧忧PPTPPT整理整理发发布布B. SIMD Processor Unit无无忧忧PPTPPT整理整理发发布布C. Decision Processor无无忧忧PPTPPT整理整理发发布布 V.proposed NoC communication无无忧忧PPTPPT整理整理发发布布VI. Low-power techniques无无忧忧PPTPPT整理整理发发布布Thanks for your attention
