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Neil Parris
Neil Parris
Neil Parris is CCI Product Manager for the ARM processor division responsible for interconnect products.

Extended System Coherency (Part 1) – Cache Coherency Fundamentals

March 23rd, 2015 by Neil Parris

The EDA industry is all about finding innovative ways of improving performance and in many ways hardware system coherency is an important part of connecting the intelligence of an SoC. It is widely known that SoCs and their component IP blocks are ever more complicated, and I am interested in easing this through extending coherency across the system. This blog is the first in a series and starts with cache coherency fundamentals.

So what do we mean by ‘coherency’?

Let’s go back to basics and ask what does coherency mean? Coherency is about ensuring all processors, or bus masters in the system see the same view of memory. For example if I have a processor which is creating a data structure then passing it to a DMA engine to move, both the processor and DMA must see the same data. If that data were cached in the CPU and the DMA reads from external DDR, the DMA will read old, stale data.


There are three mechanisms to maintain coherency:

  • Disable caching is the simplest mechanism but may cost significant CPU performance. To get the highest performance processors are pipe-lined to run at high frequency, and to run from caches which offer a very low latency. Caching of data that is accessed multiple times increases performance significantly and reduces DRAM accesses and power. Marking data as “non-cached” could impact performance and power.
  • Software managed coherency is the traditional solution to the data sharing problem. Here the software, usually device drivers, must clean or flush dirty data from caches, and invalidate old data to enable sharing with other processors or masters in the system. This takes processor cycles, bus bandwidth, and power.
  • Hardware managed coherency offers an alternative to simplify software. With this solution any cached data marked ‘shared’ will always be up to date, automatically. All processors and bus masters in that sharing domain see the exact same value.

Challenges with software coherency

A cache stores external memory contents close to the processor to reduce the latency and power of accesses. On-chip memory accesses are significantly lower power than external DRAM accesses.

Software managed coherency manages cache contents with two key mechanisms:

  • Cache Cleaning (flushing):
    • If any data stored in a cache is modified, it is marked as ‘dirty’ and must be written back to DRAM at some point in the future. The process of cleaning or flushing caches will force dirty data to be written to external memory.
  • Cache Invalidation:
    • If a processor has a local copy of data, but an external agent updates main memory then the cache contents are out of date, or ‘stale’. Before reading this data, the processor must remove the stale data from caches, this is known as ‘invalidation’ (a cache line is marked invalid). An example is a region of memory used as a shared buffer for network traffic which may be updated by a network interface DMA hardware; a processor wishing to access this data must invalidate any old stale copy before reading the new data.

Challenge 1: Software Complexity

Quote from a system architect at an application processor vendor:

“50% of debug time is spent on SW coherency issues as these are difficult to find and pinpoint”

Software coherency is hard to debug, the cache cleaning and invalidation must be done at the right time. If done too often it wastes power and CPU effort. If done too little it will result in stale data which may cause unpredictable application behavior, if not a crash. Debugging this is extremely difficult as it will present occasional data corruption.

“We would like to connect more devices with hardware coherency to simplify software and accelerate product schedules”

The quotes above are from an application processor vendor that is looking to connect more hardware accelerators and interfaces to a coherent interconnect to help reduce the time to market for new products.

Quote from a networking and modem partner:

“Only a few people in our software group understand the careful timing required to share data between the processor and radio subsystem. Scaling this to the 100’s strong software team is very difficult!”

Another partner building modem systems with a Cortex-A CPU is looking to hardware coherency to simplify software.

Challenge 2: Performance and power

Where there are high rates of sharing between requesters, the cost of software cache maintenance can be significant, and can limit performance. For example, ARM bench marking has found that for a networking application processing the header of every data packet might spend more than a third of the CPU cycles on cache maintenance. Part of the challenge is working out which data needs to be maintained. Worst case, the complete cache contents must be flushed, which may displace valuable data that needs to be read back from DRAM.

The chart below shows a simple example of DMA transfer performance for hardware vs software coherency. For this example the performance of hardware coherency increases as the amount of dirty data in processor caches increases (hit rate). This is because the software coherency version will take longer to clean and invalidate the cache if it has more dirty data.


Extending hardware coherency to the system

Hardware coherency is not a new concept. In fact, the first implementation at ARM is within the ARM11 MPCore processor. Here, up to 4 processor cores are integrated in a single cluster and can run as a “Symmetric Multi-Processor” (SMP) with visibility of each other’s L1 caches and shared L2. This technology is supported by all the latest ARM Cortex applications processors.

Extending hardware coherency to the system requires a coherent bus protocol, and in 2011 ARM released the AMBA 4 ACE specification which introduces the “AXI Coherency Extensions” on top of the popular AXI protocol. The full ACE interface allows hardware coherency between processor clusters and allows an SMP operating system to extend to more cores. With the example of two clusters, any shared access to memory can ‘snoop’ into the other cluster’s caches to see if the data is already on chip; if not, it is fetched from external memory (DDR).


The AMBA 4 ACE-Lite interface is designed for IO (or one-way) coherent system masters like DMA engines, network interfaces and GPUs. These devices may not have any caches of their own, but they can read shared data from the ACE processors. Alternatively, they may have caches but not cache shareable data.

While hardware coherency may add some complexity to the interconnect and processors, it massively simplifies the software and enables applications that would not be possible with software coherency. An example being big.LITTLE Global Task Scheduling. As much as coherency is an issue that affects the entire system, it also spans across multiple stakeholders; from SoC production to IP design to EDA itself. The wide ecosystem of EDA support from Cadence, Mentor Graphics and Synopsys amongst others really helps to drive coherency verification on a system-wide level with their continual innovations.


Cache coherency is an important concept to understand when sharing data. Disabling caches can impact performance; software coherency adds overheads and complexity; and hardware coherency manages sharing automatically which can simplify software. The AMBA 4 ACE bus interface extends hardware cache coherency outside of the processor cluster and into the system.


The next blog in the series will explore implementations of hardware coherency and look at a range of applications ranging from mobile including big.LITTLE processing and GPU compute, to enterprise including networking and servers.

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