There are many different kinds of computer architectures.
One way of categorizing computer architectures is by number of instructions executed per clock.
Many computing machines read one instruction at a time and execute it (or they put a lot of effort into acting as if they do that, even if internally they do fancy superscalar and out-of-order stuff).
I call such machines "von Neumann" machines, because all of them have a von Neumann bottleneck.
Such machines include CISC, RISC, MISC, TTA, and DSP architectures.
Such machines include accumulator machines, register machines, and stack machines.
Other machines read and execute several instructions at a time (VLIW, super-scalar), which break the one-instruction-per-clock limit, but still hit the von Neumann bottleneck at some slightly larger number of instructions-per-clock.
Yet other machines are not limited by the von Neumann bottleneck, because they pre-load all their operations once at power-up and then process data with no further instructions.
Such non-Von-Neumann machines include dataflow architectures, such as systolic architectures and cellular automata, often implemented with FPGAs, and the NON-VON supercomputer.
Another way of categorizing computer architectures is by the connection(s) between the CPU and memory.
Some machines have a unified memory, such that a single address corresponds to a single place in memory, and when that memory is RAM, one can use that address to read and write data, or load that address into the program counter to execute code. I call these machines Princeton machines.
Other machines have several separate memory spaces, such that the program counter always refers to "program memory" no matter what address is loaded into it, and normal reads and writes always go to "data memory", which is a separate location usually containing different information even when the bits of the data address happen to be identical to the bits of the program memory address.
Those machines are "pure Harvard" or "modified Harvard" machines.
Most DSPs have 3 separate memory areas -- the X ram, the Y ram, and the program memory.
The DSP, Princeton, and 2-memory Harvard machines are three different kinds of von Neumann machines.
A few machines take advantage of the extremely wide connection between memory and computation that is possible when they are both on the same chip -- computational ram or iRAM or CAM RAM -- which can be seen as a kind of non-von Neumann machine.
A few people use a narrow definition of "von Neumann machine" that does not include Harvard machines. If you are one of those people, then what term would you use for the more general concept of "a machine that has a von Neumann bottleneck", which includes both Harvard and Princeton machines, and excludes NON-VON?
Most embedded systems use Harvard architecture.
A few CPUs are "pure Harvard", which is perhaps the simplest arrangement to build in hardware: the address bus to the read-only program memory is exclusively is connected to the program counter, such as many early Microchip PICmicros.
Some modified Harvard machines, in addition, also put constants in program memory, which can be read with a special "read constant data from program memory" instruction (different from the "read from data memory" instruction).
The software running in the above kinds of Harvard machines cannot change the program memory, which is effectively ROM to that software.
Some embedded systems are "self-programmable", typically with program memory in flash memory and a special "erase block of flash memory" instruction and a special "write block of flash memory" instruction (different from the normal "write to data memory" instruction), in addition to the "read data from program memory" instruction.
Several more recent Microchip PICmicros and Atmel AVRs are self-programmable modified Harvard machines.
Another way to categorize CPUs is by their clock.
Most computers are synchronous -- they have a single global clock.
A few CPUs are asynchronous -- they don't have a clock -- including the ILLIAC I and ILLIAC II, which at one time were the fastest supercomputers on earth.
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