摘要:
The main design components underlying the implementation of physiologically faithful retina and other topographic sensory organ models on CNN universal chips is discussed. If the various retinas are implemented on a CNN universal chip, in a programmable way, it can be called a "CNN bionic eye", a device capable of performing a broad range of image processing functions similar to those performed by biological retinas. The CNN universal machine has the special properties that it is 1) programmable and 2) includes local memory. Programming is stored in analog and logical form (the analogic program) generated by an analogic programming and control unit, so the functions of the CNN universal machine can be modified as a function of complex internal and external constraints. Further, several CNN bionic eyes and other topographic sensory modalities can be combined on a single CNN universal chip, and, for more complex sensory tasks, the necessary physical microsensors to provide the input signals can be implemented on the chip, in most instances.
摘要:
This invention has 3 parts. Part 1 proposes a Cellular Neural Network or CNN universal chip (100) architecture with analog stored programs (104, 112) and time-multiplex templates. Hundreds of dedicated CNN chips may be replaced with a single programmable, real-time VLSI chip (100). Each chip (100) includes a global analogic programming unit or GAPU (102). Each chip (100) also includes a grid of enhanced cells (110), with each such cell having local units for: memory (112, 114), logic (116), communications and control (118) and output (120) functions. Part 2 proposes a unique wireless non-optical method for outputting information from the CNN analog array via electromagnetic waves generated by non-linear oscillations and chaos. Part 3 combines a set of analog, or digitally emulated, CNN universal chips (100) to design a CNN array supercomputer capable of solving non-linear partial differential equations (e.g., wave type, Navier-Stokes-type, etc.).
摘要:
The main design components underlying the implementation of physiologically faithful retina (136-144) and other topographic sensory organ models on CNN universal chips (100) is discussed. If the various retinas (136-144) are implemented on a CNN universal chip (100), in a programmable way, it can be called a 'CNN bionic eye' (174), a device capable of performing a broad range of image processing functions similar to those performed by biological retinas (136-144). The CNN universal machine (100) has the special properties that it is 1) programmable and 2) includes local memory (112). Programming is stored in analog and logical form (the analogic program) generated by an analogic programming and control unit (102), so the functions of the CNN universal machine (100) can be modified as a function of complex internal and external constraints. Further, several CNN bionic eyes (174) and other topographic sensory modalities can be combined on a single CNN universal chip (100), and, for more complex sensory tasks, the necessary physical microsensors (180) to provide the input signals can be implemented on the chip (100), in most instances.
摘要:
This invention has 3 parts. Part 1 proposes a Cellular Neural Network or CNN universal chip (100) architecture with analog stored programs (104, 112) and time-multiplex templates. Hundreds of dedicated CNN chips may be replaced with a single programmable, real-time VLSI chip (100). Each chip (100) includes a global analogic programming unit or GAPU (102). Each chip (100) also includes a grid of enhanced cells (110), with each such cell having local units for: memory (112, 114), logic (116), communications and control (118) and output (120) functions. Part 2 proposes a unique wireless non-optical method for outputting information from the CNN analog array via electromagnetic waves generated by non-linear oscillations and chaos. Part 3 combines a set of analog, or digitally emulated, CNN universal chips (100) to design a CNN array supercomputer capable of solving non-linear partial differential equations (e.g., wave type, Navier-Stokes-type, etc.).