Single Chip Supercomputers

单片超级计算机

• 批准号: 9109509
• 负责人: Tomaso Poggio
• 金额: $ 3.8万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-07-15 至 1993-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9109509&HistoricalAwards=false
• 关键词: Single; Chip; Supercomputers

The proposed research focuses on building vision systems with supercomputer capability into fast, small, low-power, analog integrated circuits. Examples where visual acuity and dexterity would be useful in products include collision sensors for cars, ground speed detectors for anti-skid lock brakes, navigation systems for mobile robots, perception sensors for micro robots and image pre-processors for remote sensing instruments. Current supercomputers are too large, too general and too complex to be cost-effective for such applications. Vision algorithms will be implemented directly in silicon through analog networks. Computation performed this way is the ultimate in parallelism, is inherently low power and compiles to a very small package size because sensors can be integrated directly with computational networks. Single chip sensor systems to be useful in the real world however, must be adaptive and self-calibrating. Designing adaptive, flexible, smart sensors requires extensive simulation. In fact, for simulations to complete in any reasonable time frame, computational assets on the order of supercomputer capability are essential. The research proposed is to utilize today's general purpose supercomputers to develop the appropriate algorithms for designing tomorrow's application specific single- chip supercomputers (analog vision chips). The Connection Machine, a 64,000 processor supercomputer, will be used for algorithm simulation and device design of these self-calibrating, adaptive vision chips. Standard computer vision algorithms bog down even the fastest computers in the world. For most vision applications, commercial supercomputers would not be feasible. For example, an automobile collision detection system must be small, low-cost, and consume little power. For such applications, general-purpose supercomputers would not be satisfactory (even if they were fast enough). The solution is to utilize special-purpose custom analog VLSI chips. These analog chips are fast, low-power, cheap and small. I have successfully built and tested more than a dozen different analog VLSI chips during my Ph.D. work at Caltech. These chips perform various smoothing, segmentation and interpolation algorithms using input from on-chip photosensors or scanned-in test data. Recently I am investigating some simple motion and stereo ideas.

拟议的研究重点是具有超级计算机能力的建筑视觉系统，以快速，小，低功率，模拟整合电路。 视力和灵活性在产品中有用的示例包括汽车的碰撞传感器，防滑锁制动器的地面速度检测器，移动机器人的导航系统，用于微型机器人的感知传感器以及用于遥感仪器的图像预处理。 当前的超级计算机太大，太一般，太复杂，无法在此类应用中具有成本效益。 视觉算法将通过模拟网络直接在硅中实现。 以这种方式执行的计算是并行性的终极，它本质上是低功率，并且可以将传感器直接与计算网络集成到非常小的包装大小。 但是，在现实世界中有用的单芯片传感器系统必须是自适应和自我校准的。 设计自适应，灵活，智能传感器需要大量的模拟。 实际上，要在任何合理的时间范围内完成模拟，超级计算机功能顺序的计算资产至关重要。 提出的研究是利用当今的通用超级计算机来开发适当的算法来设计明天的特定应用单芯片超级计算机（模拟视觉芯片）。 连接机是一种64,000个处理器超级计算机，将用于这些自我校准，自适应视觉芯片的算法模拟和设备设计。 标准的计算机视觉算法沼泽甚至是世界上最快的计算机。 对于大多数愿景应用，商业超级计算机是不可行的。 例如，汽车碰撞检测系统必须小，低成本，并且消耗的功率很少。 对于此类应用，通用超级计算机不会令人满意（即使它们足够快）。 解决方案是利用特殊用途的自定义模拟VLSI芯片。 这些模拟芯片快速，低功率，便宜且小。 在我的博士学位期间，我已经成功地建造并测试了十几个不同的模拟VLSI芯片。在加州理工学院工作。 这些芯片使用芯片光电传感器或扫描的测试数据的输入执行各种平滑，分割和插值算法。 最近，我正在调查一些简单的动作和立体声想法。