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Chapter: Embedded Systems

Memory System Mechanisms

Modern microprocessor units (MMUs) perform address translations that provide a larger virtual memory space in a small physical memory. In this section, we review both caches and MMUs.



Modern microprocessors do more than just read and write a monolithic memory. Architectural features improve both the speed and capacity of memory systems. Microprocessor clock rates are increasing at a faster rate than memory speeds, such that memories are falling further and further behind microprocessors every day. As a result, computer architects resort to caches to increase the average performance of the memory system. Although memory capacity is increasing steadily, program sizes are increasing as well, and designers may not be willing to pay for all the memory demanded by an application. Modern microprocessor units (MMUs) perform address translations that provide a larger virtual memory space in a small physical memory. In this section, we review both caches and MMUs.




Caches are widely used to speed up memory system performance. Many microprocessors architectures include caches as part of their definition. The cache speeds up average memory access time when properly used. It increases the variability of memory access times—accesses in the cache will be fast, while access to locations not cached will be slow. This variability in performance makes it especially important to understand how caches work so that we can better understand how to predict cache performance and factor variability’s into system design.


A cache is a small, fast memory that holds copies of some of the contents of main memory. Because the cache is fast, it provides higher-speed access for the CPU; but since it is small, not all requests can be satisfied by the cache, forcing the system to wait for the slower main memory. Caching makes sense when the CPU is using only a relatively small set of memory locations at any one time; the set of active locations is often called the working set.


Figure 3.6 shows how the cache support reads in the memory system. A cache controller mediates between the CPU and the memory system comprised of the main memory. The cache controller sends a memory request to the cache and main memory. If the requested location is in the cache, the cache controller forwards the location’s contents to the CPU and aborts the main memory request; this condition is known as a cache hit. If the location is not in the cache, the controller waits for the value from main memory and forwards it to the CPU; this situation is known as a cache miss.


We can classify cache misses into several types depending on the situation that generated




A compulsory miss (also known as a cold miss) occurs the first time a location is used, A capacity miss is caused by a too-large working set, and


A conflict miss happens when two locations map to the same location in the cache.



Even before we consider ways to implement caches, we can write some basic formulas for memory system performance. Let h be the hit rate, the probability that a given memory location is in the cache. It follows that 1_h is the miss rate, or the probability that the location is not in the cache. Then we can compute the average memory access time as

Where tcache is the access time of the cache and tmain is the main memory access time. The memory access times are basic parameters available from the memory manufacturer. The hit rate depends on the program being executed and the cache organization, and is typically measured using simulators. The best-case memory access time (ignoring cache controller overhead) is tcache, while the worst-case access time is tmain. Given that tmain is typically 50– 60 ns for DRAM, while tcache is at most a few nanoseconds, the spread between worst-case and best-case memory delays is substantial.


Modern CPUs may use multiple levels of cache as shown in Figure 3.7. The first-level cache (commonly known as L1 cache) is closest to the CPU, the second-level cache (L2 cache) feeds the first-level cache, and so on.


The second-level cache is much larger but is also slower. If h1 is the first-level hit rate and h2 is the rate at which access hit the second-level cache but not the first-level cache, then the average access time for a two-level cache system is


As the program’s working set changes, we expect locations to be removed from the cache to make way for new locations. When set-associative caches are used; we have to think about what happens when we throw out a value from the cache to make room for a new value. We do not have this problem in direct-mapped caches because every location maps onto a unique block, but in a set-associative cache we must decide which set will have its block thrown out to make way for the new block. One possible replacement policy is least recently used (LRU), that is, throw out the block that has been used farthest in the past. We can add relatively small amounts of hardware to the cache to keep track of the time since the last access for each block. Another policy is random replacement, which requires even less hardware to implement.


The simplest way to implement a cache is a direct-mapped cache, as shown in Figure. The cache consists of cache blocks, each of which includes a tag to show which memory location is represented by this block, a data field holding the contents of that memory, and a valid tag to show whether the contents of this cache block are valid. An address is divided into three sections. The index is used to select which cache block to check. The tag is compared against the tag value in the block selected by the index. If the address tag matches the tag value in the block, that block includes the desired memory location. If the length of the data field is longer than the minimum addressable unit, then the lowest bits of the address are used as an offset to select the required value from the data field. Given the structure of the cache, there is only one block that must be checked to see whether a location is in the cache—the index uniquely determines that block. If the access is a hit, the data value is read from the cache.


Writes are slightly more complicated than reads because we have to update main memory as well as the cache. There are several methods by which we can do this. The simplest scheme is known as write-through—every write changes both the cache and the corresponding main memory location (usually through a write buffer).


This scheme ensures that the cache and main memory are consistent, but may generate some additional main memory traffic. We can reduce the number of times we write to main memory by using a write-back policy: If we write only when we remove a location from the cache, we eliminate the writes when a location is written several times before it is removed from the cache.



The direct-mapped cache is both fast and relatively low cost, but it does have limits in its caching power due to its simple scheme for mapping the cache onto main memory. Consider a direct-mapped cache with four blocks, in which locations 0, 1, 2,and 3 all map to different blocks. But locations 4, 8,12,…all map to the same block as location 0;locations 1, 5, 9,13,…all map to a single block; and so on. If two popular locations in a program happen to map onto the same block, we will not gain the full benefits of the cache. As seen in Section 5.6, this can create program performance problems.


The limitations of the direct-mapped cache can be reduced by going to the set-associative cache structure shown in Figure 3.9.A set-associative cache is characterized by the number of banks or ways it uses, giving an n-way set-associative cache. A set is formed by all the blocks (one for each bank) that share the same index. Each set is implemented with a direct-mapped cache. A cache request is broadcast to all banks simultaneously. If any of the sets has the location, the cache reports a hit. Although memory locations map onto blocks using the same function, there are n separate blocks for each set of locations. Therefore, we can simultaneously Cache several locations that happen to map onto the same cache block. The set associative cache structure incurs a little extra overhead and is slightly slower than a direct-mapped cache, but the higher hit rates that it can provide often compensate.


The set-associative cache generally provides higher hit rates than the direct mapped cache because conflicts between a small numbers of locations can be resolved within the cache. The set-associative cache is somewhat slower, so the CPU designer has to be careful that it doesn’t slow down the CPU’s cycle time too much. A more important problem with set-associative caches for embedded program design is predictability.


Because the time penalty for a cache miss is so severe, we often want to make sure that critical segments of our programs have good behavior in the cache. It is relatively easy to determine when two memory locations will conflict in a direct-mapped cache. Conflicts in a set-associative cache are more subtle, and so the behavior of a set-associative cache is more difficult to analyze for both humans and programs. Example 3.8 compares the behavior of direct-mapped and set-associative caches.




Direct-mapped vs. set-associative caches


For simplicity, let’s consider a very simple caching scheme. We use 2 bits of the address as the tag. We compare a direct-mapped cache with four blocks and a two-way set-associative cache with four sets, and we use LRU replacement to make it easy to compare the two caches.


A 3-bit address is used for simplicity. The contents of the memory follow:



We will give each cache the same pattern of addresses (in binary to simplify picking out the index): 001, 010, 011, 100, 101, and 111.


To understand how the direct-mapped cache works, let’s see how its state evolves.


We can use a similar procedure to determine what ends up in the two-way set-associative cache. The only difference is that we have some freedom when we have to replace a block with new data. To make the results easy to understand, we use a least-recently-used replacement policy. For starters, let’s make each way the size of the original direct-mapped cache. The final state of the two-way set-associative cache follows:


Of course, this is not a fair comparison for performance because the two-way set associative cache has twice as many entries as the direct-mapped cache. Let’s use a two-way, set-associative cache with two sets, giving us four blocks, the same number as in the direct-mapped cache. In this case, the index size is reduced to 1 bit and the tag grows to 2 bits.


In this case, the cache contents are significantly different than for either the direct-mapped cache or the four-block, two-way set-associative cache. The CPU knows when it is fetching an instruction (the PC is used to calculate the address, either directly or indirectly) or data. We can therefore choose whether to cache instructions, data, or both. If cache space is limited, instructions are the highest priority for caching because they will usually provide the highest hit rates.



A cache that holds both instructions and data is called a unified cache.


Various ARM implementations use different cache sizes and organizations [Fur96]. The ARM600 includes a 4-KB, 64-way (wow!) unified instruction/data cache.The StrongARM uses a 16-KB,32-way instruction cache with a 32-byte block and a 16-KB,32-way data cache with a 32-byte block; the data cache uses a write-back strategy.


The C5510, one of the models of C55x, uses a 16-K byte instruction cache organized as a two-way set-associative cache with four 32-bit words per line. The instruction cache can be disabled by software if desired. It also includes two RAM sets that are designed to hold large contiguous blocks of code. Each RAM set can hold up to 4-K bytes of code organized as 256 lines of four 32-bitwords per line. Each RAM has a tag that specifies what range of addresses is in the RAM; it also includes a tag valid field to show whether the RAM is in use and line valid bits for each line.


Memory Management Units and Address Translation


AMMU translates addresses between the CPU and physical memory. This translation process is often known as memory mapping since addresses are mapped from a logical space into a physical space. MMUs in embedded systems appear primarily in the host processor. It is helpful to understand the basics of MMUs for embedded systems complex enough to require them.


Many DSPs, including the C55x, do not use MMUs. Since DSPs are used for compute-intensive tasks, they often do not require the hardware assist for logical address spaces. Early computers used MMUs to compensate for limited address space in their instruction sets. When memory became cheap enough that physical memory could be larger than the address space defined by the instructions, MMUs allowed software to manage multiple programs in a single physical memory, each with its own address space.


Because modern CPUs typically do not have this limitation, MMUs are used to provide virtual addressing. As shown in Figure 3.10, the MMU accepts logical addresses from the CPU. Logical addresses refer to the program’s abstract address space but do not correspond to actual RAM locations. The MMU translates them from tables to physical addresses that do correspond to RAM. By changing the MMU’s tables, you can change the physical location at which the program resides without modifying the program’s code or data. (We must, of course, move the program in main memory to correspond to the memory mapping change.)


Furthermore, if we add a secondary storage unit such as flash or a disk, we can eliminate parts of the program from main memory. In a virtual memory system, the MMU keeps track of which logical addresses are actually resident in main memory; those that do not reside in main memory are kept on the secondary storage device.



When the CPU requests an address that is not in main memory, the MMU generates an exception called a page fault. The handler for this exception executes code that reads the requested location from the secondary storage device into main memory.


The program that generated the page fault is restarted by the handler only after the required memory has been read back into main memory, and The MMU’s tables have been updated to reflect the changes.


Of course, loading a location into main memory will usually require throwing something out of main memory. The displaced memory is copied into secondary storage before the requested location is read in. As with caches, LRU is a good replacement policy.


There are two styles of address translation: segmented and paged. Each has advantages and the two can be combined to form a segmented, paged addressing scheme. As illustrated in Figure 3.11, segmenting is designed to support a large, arbitrarily sized region of memory, while pages describe small, equally sized regions.


A segment is usually described by its start address and size, allowing different segments to be of different sizes. Pages are of uniform size, which simplifies the hardware required for address translation. A segmented, paged scheme is created by dividing each segment into pages and using two steps for address translation.


Paging introduces the possibility of fragmentation as program pages are scattered around physical memory.


In a simple segmenting scheme, shown in Figure 3.12,the MMU would maintain a segment register that describes the currently active segment. This register would point to the base of the current segment. The address extracted from an instruction (or from any other source for addresses, such as a register) would be used as the offset for the address. The physical address is formed by adding the segment base to the offset. Most segmentation schemes also check the physical address against the upper limit of the segment by extending the segment register to include the segment size and comparing the offset to the allowed size.


The translation of paged addresses requires more MMU state but a simpler calculation. As shown in Figure 3.13, the logical address is divided into two sections, including a page number and an offset. The page number is used as an index into a page table, which stores the physical address for the start of each page. However since all pages have the same size and it is easy to ensure that page boundaries fall on the proper boundaries, the MMU simply needs to


Concatenate the top bits of the page starting address with the bottom bits from the page offset to form the physical address. Pages are small, typically between 512 bytes and 4 KB. As a result, the page table is large for architecture with a large address space. The page table is normally kept in main memory, which means that an address translation requires memory access.



The page table may be organized in several ways, as shown in Figure 3.14. The simplest scheme is a flat table. The table is indexed by the page number and each entry holds the page descriptor. A more sophisticated method is a tree. The root entry of the tree holds pointers to pointer tables at the next level of the tree; each pointer table is indexed by a part of the page number. We eventually (after three levels, in this case) arrive at a descriptor table that includes the page descriptor we are interested in. A tree-structured page table incurs some overhead for the pointers, but it allows us to build a partially populated tree. If some part of the address space is not used, we do not need to build the part of the tree that covers it.


The efficiency of paged address translation may be increased by caching page translation information. A cache for address translation is known as a translation look aside buffer (TLB).The MMU reads the TLB to check whether a page number is currently in the TLB cache and, if so, uses that value rather than reading from memory.


Virtual memory is typically implemented in a paging or segmented, paged scheme so that only page-sized regions of memory need to be transferred on a page fault. Some extensions to both segmenting and paging are useful for virtual memory:


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