ポイントペア特徴量とカーブセット特徴量を用いた物体認識と姿勢推定アルゴリズム

要約のみTohoku University橋本浩一課

Architecting SkyBridge-CMOS

As the scaling of CMOS approaches fundamental limits, revolutionary technology beyond the end of CMOS roadmap is essential to continue the progress and miniaturization of integrated circuits. Recent research efforts in 3-D circuit integration explore pathways of continuing the scaling by co-designing for device, circuit, connectivity, heat and manufacturing challenges in a 3-D fabric-centric manner. SkyBridge fabric is one such approach that addresses fine-grained integration in 3-D, achieves orders of magnitude benefits over projected scaled 2-D CMOS, and provides a pathway for continuing scaling beyond 2-D CMOS. However, SkyBridge fabric utilizes only single type transistors in order to reduce manufacture complexity, which limits its circuit implementation to dynamic logic. This design choice introduces multiple challenges for SkyBridge such as high switching power consumption, susceptibility to noise, and increased complexity for clocking. In this thesis we propose a new 3-D fabric, similar in mindset to SkyBridge, but with static logic circuit implementation in order to mitigate the afore-mentioned challenges. We present an integrated framework to realize static circuits with vertical nanowires, and co-design it across all layers spanning fundamental fabric structures to large circuits. The new fabric, named as SkyBridge-CMOS, introduces new technology, structures and circuit designs to meet the additional requirements for implementing static circuits. One of the critical challenges addressed here is integrating both n-type and p-type nanowires. Molecular bonding process allows precise control between different doping regions, and novel fabric components are proposed to achieve 3-D routing between various doping regions. Core fabric components are designed, optimized and modeled with their physical level information taken into account. Based on these basic structures we design and evaluate various logic gates, arithmetic circuits and SRAM in terms of power, area footprint and delay. A comprehensive evaluation methodology spanning material/device level to circuit level is followed. Benchmarking against 16nm 2-D CMOS shows significant improvement of up to 50X in area footprint and 9.3X in total power efficiency for low power applications, and 3X in throughput for high performance applications. Also, better noise resilience and better power efficiency can be guaranteed when compared with original SkyBridge fabrics

KAPLA: Pragmatic Representation and Fast Solving of Scalable NN Accelerator Dataflow

Dataflow scheduling decisions are of vital importance to neural network (NN) accelerators. Recent scalable NN accelerators support a rich set of advanced dataflow techniques. The problems of comprehensively representing and quickly finding optimized dataflow schemes thus become significantly more complicated and challenging. In this work, we first propose comprehensive and pragmatic dataflow representations for temporal and spatial scheduling on scalable multi-node NN architectures. An informal hierarchical taxonomy highlights the tight coupling across different levels of the dataflow space as the major difficulty for fast design exploration. A set of formal tensor-centric directives accurately express various inter-layer and intra-layer schemes, and allow for quickly determining their validity and efficiency. We then build a generic, optimized, and fast dataflow solver, KAPLA, which makes use of the pragmatic directives to explore the design space with effective validity check and efficiency estimation. KAPLA decouples the upper inter-layer level for fast pruning, and solves the lower intra-layer schemes with a novel bottom-up cost descending method. KAPLA achieves within only 2.2% and 7.7% energy overheads on the result dataflow for training and inference, respectively, compared to the exhaustively searched optimal schemes. It also outperforms random and machine-learning-based approaches, with more optimized results and orders of magnitude faster search speedup