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.

拟议的研究重点是将具有超级计算机能力的视觉系统构建成快速、小、低功耗的模拟集成电路。视觉敏锐度和灵活性在产品中有用的例子包括用于汽车的碰撞传感器、用于防滑锁死刹车的地速检测器、用于移动机器人的导航系统、用于微型机器人的感知传感器和用于遥感仪器的图像预处理器。目前的超级计算机太大、太通用、太复杂，无法满足此类应用的成本效益。视觉算法将通过模拟网络直接在硅中实现。由于传感器可以直接与计算网络集成，因此以这种方式执行的计算具有终极的并行性，固有的低功耗，并且编译成非常小的包尺寸。然而，单片传感器系统要想在现实世界中有用，就必须具有自适应和自校准能力。设计自适应、灵活、智能的传感器需要广泛的模拟。事实上，要在任何合理的时间框架内完成模拟，超级计算机能力量级的计算资产是必不可少的。提出的研究是利用今天的通用超级计算机来开发合适的算法，以设计未来专用的单芯片超级计算机(模拟视觉芯片)。Connection Machine是一台64,000处理器的超级计算机，将用于这些自校准、自适应视觉芯片的算法模拟和设备设计。即使是世界上最快的计算机，标准的计算机视觉算法也会陷入困境。对于大多数视觉应用来说，商业超级计算机是不可行的。例如，汽车碰撞检测系统必须是小型、低成本、低功耗的。对于这样的应用，通用超级计算机将不能令人满意(即使它们足够快)。解决方案是利用特殊用途的定制模拟VLSI芯片。这些模拟芯片具有速度快、功耗低、价格便宜、体积小等特点。在加州理工的博士工作期间，我成功地制造并测试了十几种不同的模拟VLSI芯片。这些芯片使用来自片上光电传感器或扫描输入测试数据的输入执行各种平滑、分割和内插算法。最近，我正在研究一些简单的运动和立体声想法。