Single Chip Supercomputers

提出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、小型、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汽车碰撞传感器、防滑锁制动器的地面速度探测器、移动机器人的导航系统、微型机器人的感知传感器和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，不符合这种应用的成本效益。视觉算法将通过模拟网络直接在硅片上实现。以这种方式执行的计算是最终的并行性，具有固有的低功耗和编译成非常小的封装尺寸，因为传感器可以直接与计算网络集成。然而，单芯片传感器系统要在现实世界中发挥作用，必须具有自适应和自校准能力。设计自适应的、灵活的、智能的传感器需要大量的仿真。事实上，为了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。本研究的目的是利用今天的通用超级计算机来开发适当的算法，以设计未来的应用特定的单芯片超级计算机（模拟视觉芯片）。连接机是一台拥有64,000个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。标准的计算机视觉算法甚至会让世界上最快的计算机陷入困境。对于大多数视觉应用来说，商用超级计算机是不可行的。例如，汽车碰撞检测系统必须体积小、成本低、功耗小。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。这些模拟芯片速度快、功耗低、价格便宜、体积小。在加州理工学院攻读博士学位期间，我已经成功地构建和测试了十几种不同的模拟VLSI芯片。这些芯片执行各种平滑，分割和插值算法使用从片上的光电传感器或扫描入测试数据的输入。最近我正在研究一些简单的运动和立体的想法。

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.