Difference between Harvard Architecture and Von Neumann architecture

Harvard architecture has separate data and instruction busses, allowing transfers to be performed simultaneously on both busses. A von Neumann architecture has only one bus which is used for both data transfers and instruction fetches, and therefore data transfers and instruction fetches must be scheduled - they can not be performed at the same time.

It is possible to have two separate memory systems for a Harvard architecture. As long as data and instructions can be fed in at the same time, then it doesn't matter whether it comes from a cache or memory. But there are problems with this. Compilers generally embed data (literal pools) within the code, and it is often also necessary to be able to write to the instruction memory space, for example in the case of self modifying code, or, if an ARM debugger is used, to set software breakpoints in memory. If there are two completely separate, isolated memory systems, this is not possible. There must be some kind of bridge between the memory systems to allow this.

Using a simple, unified memory system together with a Harvard architecture is highly inefficient. Unless it is possible to feed data into both busses at the same time, it might be better to use a von Neumann architecture processor.

Use of caches

At higher clock speeds, caches are useful as the memory speed is proportionally slower. Harvard architectures tend to be targeted at higher performance systems, and so caches are nearly always used in such systems.

Von Neumann architectures usually have a single unified cache, which stores both instructions and data. The proportion of each in the cache is variable, which may be a good thing. It would in principle be possible to have separate instruction and data caches, storing data and instructions separately. This probably would not be very useful as it would only be possible to ever access one cache at a time.

Caches for Harvard architectures are very useful. Such a system would have separate caches for each bus. Trying to use a shared cache on a Harvard architecture would be very inefficient since then only one bus can be fed at a time. Having two caches means it is possible to feed both buses simultaneously....exactly what is necessary for a Harvard architecture.

This also allows to have a very simple unified memory system, using the same address space for both instructions and data. This gets around the problem of literal pools and self modifying code. What it does mean, however, is that when starting with empty caches, it is necessary to fetch instructions and data from the single memory system, at the same time. Obviously, two memory accesses are needed therefore before the core has all the data needed. This performance will be no better than a von Neumann architecture. However, as the caches fill up, it is much more likely that the instruction or data value has already been cached, and so only one of the two has to be fetched from memory. The other can be supplied directly from the cache with no additional delay. The best performance is achieved when both instructions and data are supplied by the caches, with no need to access external memory at all.

This is the most sensible compromise and the architecture used by ARMs Harvard processor cores. Two separate memory systems can perform better, but would be difficult to implement.

The name Harvard Architecture comes from the Harvard Mark I relay-based computer. The most obvious characteristic of the Harvard Architecture is that it has physically separate signals and storage for code and data memory. It is possible to access program memory and data memory simultaneously. Typically, code (or program) memory is read-only and data memory is read-write. Therefore, it is impossible for program contents to be modified by the program itself.

The von Neumann Architecture is named after the mathematician and early computer scientist John von Neumann. von Neumann machines have shared signals and memory for code and data. Thus, the program can be easily modified by itself since it is stored in read-write memory.

	Erstellt von Max Williams vor mehr als 7 Jahre

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