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Cortex A57, Nintendo Swap’s CPU – Chips and Cheese

Cortex A57, Nintendo Swap’s CPU – Chips and Cheese

2023-12-13 09:11:26

Within the early 2010s, Arm’s 32-bit cores had established themselves in cell telephones and tablets. However rising reminiscence capacities in these units meant Arm must go 64-bit in the end. On high of that, Arm had server ambitions – and 64-bit help was important for the server market. That brings us to the Cortex A57. Alongside the beforehand lined Cortex A53, the A57 represents Arm’s first technology of 64-bit succesful cores.

We’re going to cowl the Cortex A57 as applied within the Nintendo Swap’s Nvidia Tegra X1. The Tegra X1 targets a variety of purposes together with cell units and vehicles. It focuses on offering excessive GPU efficiency in a restricted energy envelope, making it excellent for a conveyable gaming console just like the Swap. Tegra X1 consumes 117,6 mm2 on TSMC’s 20 nm (20 SoC) course of and makes use of a quad core A57 cluster to supply the majority of its CPU energy. Every Cortex A57 core consumes just below 2 mm2 of space, and the quad core A57 cluster takes 13.16 mm2.

The SoC additionally accommodates a cluster of 4 A53 cores for energy environment friendly processing, however Nintendo has chosen to not use them. A LPDDR4 reminiscence controller connects the Tegra X1 to 4 GB of DRAM with as much as 25.6 GB/s of theoretical bandwidth.

Within the Tegra X1, the Cortex A57 cores clock as much as 1.78 GHz. Below Ubuntu, they take round a 3rd of a second to go from 1.2 GHz as much as their most pace.


Cortex A57 is a 3-wide out-of-order core with giant most reordering capability, however comparatively small schedulers and different supporting constructions. Arm’s designers have positively prioritized low energy and space over excessive efficiency, however A57 is extra bold of a design than older low energy cores like AMD’s Jaguar or Intel’s Silvermont. It brings some distinctive options to the desk too, like a versatile register file the place a number of 32-bit entries can be utilized to carry 64-bit or 128-bit outcomes.

The Cortex A57 might be applied in clusters of as much as 4 cores with a shared L2 cache. L2 capability might be set to 512 KB, 1 MB, or 2 MB. Nvidia has chosen the two MB choice for the Tegra X1, buying and selling some space for larger efficiency. Not like fashionable Arm cores, L1 capacities aren’t configurable. They’re fastened at 48 KB for the instruction cache and 32 KB for the information cache. Nonetheless, implementers can flip off error correction for the L1 caches to realize some space and energy effectivity at the price of reliability.

Arm’s diagram of a quad core A57 cluster, from the A57 technical reference handbook

Frontend: Department Prediction

A CPU’s department predictor tells the pipeline the place to go. Quick and correct department prediction helps each efficiency and energy effectivity by holding the pipeline fed and avoiding wasted work. However department predictors themselves take space and energy, so CPU designers have to seek out the best stability when deciding how subtle of a department predictor to implement.

Low energy CPUs just like the Cortex A57 don’t have the hypothesis distance or core throughput of desktop CPUs, so Arm has given the A57 a modest department predictor. The predictor makes use of international historical past, which implies it predicts whether or not a department is taken or not taken relying on how earlier branches within the instruction stream behaved (and never essentially the historical past of the department being predicted). A57’s predictor can’t monitor extraordinarily lengthy patterns like department predictors on desktops. However it needs to be enough for a low energy design, and avoids chasing diminishing returns.

To reduce department delays, Cortex A57 has two ranges of caching for department targets, or department goal buffers (BTBs). The primary degree BTB has 64 entries and handles taken branches with a 1 cycle delay. A second degree BTB with 2048 to 4096 entries helps A57 take care of bigger department footprints. Targets that come from the L2 BTB incur a 2 cycle delay.

Department latency drastically will increase as soon as the take a look at exceeds L1 instruction cache capability, suggesting the L2 BTB is tied to the L1i.

Oblique branches can bounce to a number of completely different locations as an alternative of a hard and fast one. They’re more difficult to foretell as a result of the frontend has to decide on between a number of targets as an alternative of simply guessing whether or not the department is taken or not taken. Nonetheless, oblique prediction functionality is essential as a result of oblique branches have a tendency to indicate up in object oriented languages.

A57’s oblique predictor can monitor as much as 16 completely different targets for a single department. In whole, it was in a position to monitor 128 oblique targets with out vital penalty. That was achieved with 64 branches, every alternating between two targets. Arm’s slides recommend the oblique predictor has 512 entries.

Returns are a particular case of oblique branches. A perform can return to a number of completely different name websites, however monitoring them is usually so simple as pushing the deal with of the following instruction when encountering a name instruction, then popping the deal with off when hitting a corresponding return.

Take a look at outcomes weren’t as simple to interpret as on different CPUs. Nonetheless, there’s a little bit of an inflection level round 32 nested calls. Maybe A57 has a 32 entry return stack.

Frontend: Fetch and Decode

As soon as the department predictor has determined the place to go, the core has to fetch instruction bytes and decode them. Cortex A57’s 48 KB 3-way instruction cache is the primary cease for directions. It has optionally available parity safety at 4 byte granularity for knowledge and 36-bit granularity for tags. Parity errors are resolved by invalidating the road with the error and reloading it from L2. Digital to bodily deal with translation is dealt with by a totally associative 48 entry instruction TLB.

On an instruction cache miss, A57 generates a fill request to L2. To enhance code fetch bandwidth from L2, the core additionally prefetches the following sequential line. From testing, L2 code fetch bandwidth is low at about 1 IPC. Desktop CPUs and more moderen Arm cores can obtain higher code throughput from L2, however by some means A57 does higher than the newer Cortex A72. On each A57 and A72, the comparatively giant 48 KB instruction cache ought to assist scale back misses in comparison with the 32 KB instruction caches typical on different CPUs.

After directions are fetched, they’re queued right into a 32 entry buffer that feeds into the decoders. Directions are then translated into micro-ops. A57’s decoders additionally do register renaming to take away false write-after-write dependencies. Not like renamers on modern x86 CPUs or fashionable Arm cores, A57’s renamer doesn’t carry out transfer elimination or break dependencies with zeroing idioms.

Out of Order Execution Engine

A CPU’s out-of-order execution engine is chargeable for monitoring in-flight directions, executing them as their inputs change into obtainable, and committing their outcomes whereas respecting ISA guidelines. Cortex A57’s execution engine can have as much as 40 instruction bundles in flight.

Every bundle can comprise a number of directions, however NOPs look like an exception. A NOP consumes a complete bundle, explaining why a naive reorder buffer capability take a look at with NOPs reveals 40 entries. Nonetheless, at the very least eight math directions might be saved collectively in a single bundle. That features 32-bit or 64-bit integer provides in addition to 128-bit vector ones. Reminiscence accesses and branches eat a complete bundle like NOPs do.

Unified Register File

Not like most CPUs, Cortex A57 makes use of a 128 entry unified register file to deal with renaming for each integer and floating level registers. CPUs usually have separate integer and FP register recordsdata as a result of integer registers want low latency, whereas FP registers need to be wider to deal with vector execution. Cortex A57’s scheme could possibly be an space saving measure as a result of it lets A57 work with one register file as an alternative of two.

Most reordering capability is achieved with 32-bit values

As a result of the register file’s entries are 32 bits extensive, A57 is at its finest when dealing with 32-bit integer code. Floating level code will see competition between integer and FP directions for shared register file capability. 64-bit or 128-bit values are supported by allocating a number of 32-bit values. The core nonetheless has enough renaming capability for 64-bit values, however issues get tight with 128-bit vectors.

Curiously, A57’s finally ends up a bit off its nominal register file capability if I combine completely different register widths. I believe A57’s register file acts like that of GPUs, and has alignment restrictions when utilizing a number of 32-bit entries to retailer a bigger worth.

Take a look at Measured Renaming Capability Renamed Register File Utilization
Blended 64-bit Integer and 128-bit Vector Registers 41 20x 64-bit (40x 32-bit entries)
21x 128-bit (84x 32-bit entries)
2x 32-bit for pointer chasing hundreds
2 unused?
Blended 32-bit Integer and 128-bit Vector Registers 41 20x 32-bit
21x 128-bit (84x 32-bit entries)
2x 32-bit for pointer chasing hundreds
22 unused?
Blended 32-bit FP and 128-bit Vector Registers 42 21x 32-bit
21x 128-bit (84x 32-bit entries)
2x 32-bit for pointer chasing hundreds
21 unused?

Relying on how smaller registers are allotted, the renamer won’t have the ability to discover a contiguous aligned block of 4 32-bit registers when it wants area to retailer a 128-bit worth. So whereas A57 makes use of register storage extra effectively than fashionable CPUs, it’s not excellent.

When directions retire, their outcomes are copied from the renamed register file right into a separate architectural register file (ARF). Like on Intel’s unique P6, this scheme is probably simpler to implement as a result of renamed registers might be freed when an instruction retires. Exception restoration can also be simple as a result of known-good register state is held in a single place. Nonetheless, the ARF scheme requires extra knowledge motion and thus consumes extra energy. Intel moved to a PRF scheme with Sandy Bridge, and Arm did the identical with Cortex A73.

Arm’s newer Cortex A72 presumably makes use of a scheme just like the A57. I went again over knowledge collected on that CPU and there are actually similarities to A57 knowledge. Once I examined the A72, I rushed as a result of I used to be paying for a Graviton 1 cloud occasion, and thus had restricted time to jot down completely different exams to discover the core. I’ll need to revisit A72 when work and actual life calls for give me a break.


A57 makes use of a principally distributed scheduling scheme with small devoted schedulers feeding every execution unit. Many of the schedulers have eight entries. The 2 exceptions are the department scheduler, which has 12 entries, and the load/retailer AGUs, which share a 16 entry scheduling queue. Scheduling capability is prone to restrict A57’s reordering capability earlier than the register file or reorder buffer fills.

Instruction Class A57 Scheduling Entries Accessible Skylake Scheduling Entries Accessible
Frequent integer operations (add, shift, bitwise operations) 16 58
FP/Vector operations 16 58
Reminiscence operations 16 39

Integer Execution Models

Like many low energy CPUs, A57 has two integer pipelines able to dealing with the commonest operations. Multi-cycle integer operations go to a separate pipe. Multiplies go down this pipe and take 5 cycles to provide a 64-bit end result. Branches get a separate port as properly, seemingly to make sure they’re resolved as rapidly as attainable to reduce mispredict penalties.

From the A57 optimization information

In idea, A57 can execute 4 integer operations per cycle. In observe, that may not often happen as a result of branches and multi-cycle integer operations are far much less frequent than easy ones.

Floating Level and Vector Execution

FP and vector operations are dealt with by a pair of ports, every fed by an 8 entry scheduler. Arm’s optimization handbook suggests each ports can deal with fundamental FP operations like addition and multiplication, however I used to be unable to get two scalar FP operations per cycle. 128-bit packed FP operations additionally execute at one per cycle, so Cortex A57 does have 128 bits of whole FP throughput however unusually can’t execute two FP directions per cycle.

Take a look at Cortex A57 Skylake
32-bit Scalar FP Provides 1 per cycle
5 cycle latency
2 per cycle
4 cycle latency
32-bit Scalar FP Multiplies 0.83 per cycle
6 cycle latency
2 per cycle
4 cycle latency
32-bit Scalar FP Fused Multiply Add 1 per cycle
10 cycle latency
2 per cycle
4 cycle latency
Blended 32-bit Scalar FP Provides and Multiplies 0.83 per cycle
128-bit packed FP32 Provides 1 per cycle
5 cycle latency
2 per cycle
4 cycle latency
128-bit packed FP32 Multiplies 1 per cycle
6 cycle latency
2 per cycle
4 cycle latency
128-bit packed FP32 Fused Multiply Add 1 per cycle
10 cycle latency
2 per cycle
4 cycle latency

ARM’s ASIMD/NEON instruction set gives fmla and fmadd directions for fused multiply add operations. These execute at one per cycle on Cortex A57, doubling FP throughput. Nonetheless, latency could be very excessive at 10 cycles.

Vector integer operations get pleasure from higher efficiency. Each pipelines can deal with 128-bit packed integer provides and different fundamental operations. Latency nonetheless continues to be excessive. Vector integer provides have 3 cycles of latency, and multiplies have 4 cycle latency. 128-bit packed integer multiplies solely use the primary FP pipe, and execute at a fee of 1 per two cycles.


Reminiscence operations are dealt with by a pair of pipelines. One handles hundreds and the opposite handles shops. Cortex A57 can have 32 hundreds and 12 shops in-flight. The small retailer queue dimension will seemingly restrict reordering capability. Zen 4 already sees its store queue fill often. If we think about that A57 is designed to maintain round 128 directions in flight, the shop queue covers much less of the core’s reordering window than on Zen 4.

Not like newer Arm cores and modern desktop CPUs, Cortex A57 doesn’t attempt to predict whether or not reminiscence operations will likely be unbiased. Subsequently, hundreds are stalled till all prior retailer addresses have been resolved. For dependent hundreds, retailer forwarding latency is usually seven cycles. Forwarding latency is doubled if a load partially overlaps a 16B aligned retailer, however in any other case there are not any notable gradual paths.

Cortex A57 interprets program-visible 48-bit digital addresses to 44-bit bodily addresses with a totally associative 32 entry DTLB. With 4K pages, these 32 entries ought to cowl 128 KB of deal with area. DTLB misses might be caught by a unified 1024 entry 4-way set associative L2 TLB. This L2 TLB additionally handles misses from the instruction TLB.

44-bit bodily addressing lets Cortex A57 deal with as much as 16 TB of reminiscence. Arm’s newer cores just like the Cortex X2 solely help 40-bit physical addresses, with bigger bodily deal with functionality reserved for Arm’s Neoverse line. A57 was anticipated to tackle each shopper and server roles, and needed to make a compromise.

Cache and Reminiscence Latency

Reminiscence accesses first test Cortex A57’s 32 KB 2-way set associative knowledge cache. The info cache has optionally available ECC safety with 4 byte granularity. Knowledge cache accesses usually have 4 cycle latency, or 5 cycles for listed addressing. A57 suffers closely from deal with translation penalties when utilizing 4K pages. Latency spikes to 16 cycles on the 12 KB take a look at dimension. The spike goes away when utilizing large pages, suggesting we’re by some means getting TLB misses although the take a look at array needs to be properly inside TLB protection.

L1 misses go to a shared 2 MB L2 cache. The L2 has obligatory ECC safety for each knowledge and tags, serving to with knowledge integrity. To avoid wasting on space and energy, the L2 cache makes use of a random substitute coverage. Meaning the cache doesn’t need to retailer metadata on which traces had been most lately or often used, however can result in sub-optimal choices on what knowledge to kick out when bringing new traces in.

Arm lets implementers configure L2 latency to maintain clocks up with bigger L2 choices. It’s not possible to inform if Nvidia used any of those register slicing choices, however testing confirmed the L2 has 22 cycles of load-to-use latency. Nonetheless, including L2 TLB latency brings this to over 50 cycles. Lastly, DRAM entry takes round 250 cycles.

Intel’s Skylake is roughly modern with Nvidia’s Tegra X1 and enjoys higher latency all through the reminiscence hierarchy. Entry to the i5-6600K’s 6 MB final degree cache takes about 11.2 ns, and that features a few cycles to get an deal with translation from the L2 TLB.

Reminiscence latency on the Nintendo Swap is excessive by desktop requirements. LPDDR4 seemingly contributes to larger latency, however is a needed design selection for a bandwidth hungry low energy gadget.

Core to Core Latency

Multi-core techniques have to make sure all cores have a coherent view of reminiscence. Cortex A57’s L2 cache handles this by being strictly inclusive of L1 knowledge cache contents, and sustaining duplicate copies of the L1 knowledge cache tag arrays. We will take a look at how briskly two cores can change knowledge through the use of __sync_bool_compare_and_swap to bounce a worth between them.

Although transfers are happening inside a core cluster, latency is larger than cross-cluster latency on AMD’s Zen.

That stated, a very good program will keep away from shuffling knowledge between cores. Tegra X1’s larger core to core latency ought to have minimal impression on efficiency.


Cortex A57’s L1 knowledge cache can service a load and a retailer each cycle. With 128-bit vector accesses, that offers 28 GB/s of L1D learn bandwidth. Surprisingly, L1 bandwidth falls off after 16 KB. As with latency, utilizing hugepages makes the dip go away, suggesting deal with translation penalties are reducing into bandwidth. I noticed the identical habits with A72.

Cortex A57’s cluster-wide L2 cache is 16-way set associative, and has 2 MB of capability on the Tegra X1. The L2 tags are divided into two banks, letting it service two requests per cycle. Every tag financial institution covers 4 knowledge banks. For a single core, the L2 can present about 8 bytes per cycle of learn bandwidth.

Due to banking, we get a pleasant bandwidth improve with two cores loaded. Nonetheless, L2 bandwidth will increase taper off as we load extra cores. In distinction, Intel’s L3 cache has as many banks as there are cores, permitting for wonderful bandwidth scaling. The Core i5-6600K’s L3 can ship a large quantity of bandwidth, and a single Skylake core can get extra cache bandwidth than all 4 of Tegra X1’s Cortex A57 cores mixed.

See Also

Intel’s quad-banked L3 cache delivers higher per-cycle efficiency too, at simply above 62 bytes per cycle with a reminiscence bandwidth take a look at working throughout 4 cores. Cortex A57’s shared L2 is caught at 15.72 bytes per cycle. L2 write bandwidth is considerably decrease seemingly attributable to overhead from read-for-ownership requests.

L2 misses are usually glad by the DRAM controller. Tegra X1’s LPDDR4 setup ought to present 25.6 GB/s of theoretical bandwidth. From the CPU cores, we get just below 30% of that determine. Bandwidth stops rising after 2 to three cores are loaded, and tops out under 8 GB/s.

For comparability, a Core i5-6600K with twin channel DDR4-2133 achieved 28.6 GB/s in the identical take a look at. That’s 83% of the theoretical 34.1 GB/s obtainable. Extremely, Skylake enjoys larger reminiscence bandwidth than Cortex A57 does from its L2.

Write bandwidth reveals the same story. A57’s reminiscence bandwidth continues to be poor and much under what Skylake enjoys.

Due to agent_x007 for contributing the bandwidth take a look at run on Jaguar

AMD’s Jaguar gives one other comparability level. It equally makes use of a 2 MB shared L2 cache, however AMD enjoys twice as a lot L2 bandwidth. Tegra X1 implements a extra fashionable 64-bit LPDDR4 interface in comparison with Jaguar’s single channel DDR3 setup, however Cortex A57 fails to attain larger reminiscence bandwidth.

Some Mild Benchmarking

I’m gonna pivot right here with a deal with core width. We already understand how Skylake will stack up towards Cortex A57 if I benchmark the 2 face to face. A comparability to Arm’s later Cortex A73 needs to be extra enjoyable, as a result of A73 is a 2-wide core that shares some weaknesses with A57 like a small scheduler. Meaning A73 can’t match A57 in sustained throughput. Nonetheless, A73 does run at the next 2.2 GHz clock pace.

Right here, I’m testing how lengthy it takes for 7-Zip to compress a big 2.67 GB file. So as to add some selection, I’m additionally utilizing libx264 to transcode a 4K video all the way down to 720P.

Within the compression workload, Cortex A57 pulls forward. Greater core width wins over a narrower however sooner clocked core. Nonetheless, it’s a small lead of lower than 5%. With video encoding, A73 wins. However once more, the distinction is lower than 5%. Cortex A73 has simply 1 MB of L2 cache in comparison with A57’s 2 MB, so Arm’s newer core is doing fairly properly.

The shut outcomes are as a result of A73 improves on facets aside from core width, and we are able to dig into that utilizing efficiency counters. As an apart, efficiency counters aren’t comparable throughout opinions as a result of I’m choosing the closest set of occasions to finest examine the 2 cores. For instance, Cortex A57 can not rely branches and department mispredicts at retirement. It solely has occasions that rely on the execute stage. A73 introduces at-retirement occasions for branches and mispredicted branches.

Firstly of the pipeline, A73 has a stronger department predictor. Mispredicts per instruction decreased by 20% and 27% in 7-Zip and libx264 respectively. A73 thus wastes much less energy and time engaged on the unsuitable stuff.

On the instruction fetch stage, A73 loses width however will increase instruction cache capability to 64 KB. For libx264, this reduces instruction cache misses per instruction by 53%. Fetching from L2 or past prices extra energy than doing so from a primary degree cache, so A73 cuts down energy spent transferring instruction bytes round.

7-Zip additionally advantages from a bigger instruction cache, however A57’s 48 KB cache was already giant sufficient.

The info aspect sees much less change. A73 and A57 each have a 32 KB knowledge cache, so hitrates are related. Each examined purposes see fairly just a few L1D refills, however libx264 struggles extra.

Lastly, we are able to have a look at L2 misses. I’m utilizing the l2d_cache_refill occasion from perf on each CPUs, however what the occasion is counting doesn’t appear comparable throughout the 2 CPUs. Cortex A73 counts extra L2 knowledge refills than L1D refills, so it may have a really aggressive L2 prefetcher.

Cortex A57’s L2 prefetcher can generate as much as eight prefetches after a L2 miss, although all need to be throughout the identical 4K web page. Arm’s Cortex-A73 MPCore Processor Technical Reference Handbook mentions a L2 prefetcher, however doesn’t say if it’s extra aggressive. Nonetheless, A73’s L2 can help 48 pending fills, whereas A57’s can solely help as much as 20. A73 could possibly be exploiting that elevated reminiscence degree parallelism functionality to scale back efficiency loss from a smaller L2.

Ultimate Phrases

Taken in isolation, Arm’s Cortex A57 is a good low energy core with wonderful register renaming capability for 32-bit integer code and satisfactory department prediction. Throughout the execution engine, the core’s major weaknesses are excessive execution latency for floating level directions, small scheduler capability, and a small retailer queue. A few of these weaknesses are current on different low energy designs too, so it’s onerous to fault Arm for that.

The place Cortex A57 actually struggles is the reminiscence subsystem. At a excessive degree, the core-private 32 KB knowledge caches and a pair of MB shared final degree cache are typical for a low energy design. AMD makes use of the same setup in Jaguar just a few years prior. Nonetheless, A57’s TLB setup suffers from extraordinarily poor efficiency. The core suffers inexplicable L1 DTLB misses even when accessing arrays that ought to match inside its 128 KB protection. When L1 DTLB msises occur, the 1024 entry L2 TLB performs poorly and provides over 20 further cycles of latency. L2 TLBs on most different CPUs have lower than 10 cycles of latency. Lastly, each the L2 cache and system reminiscence endure from poor bandwidth.

As a low energy cellphone and pill core, Cortex A57 is a suitable design for its time. Subsequent years would see Arm develop more and more performant low energy cores as enhancements in course of expertise allowed extra subtle microarchitectures to suit inside just a few watts.

Because the Nintendo Swap’s CPU, Cortex A57 is weak and never akin to even outdated desktop cores. Trendy recreation builders are in a position to port video games to this platform, however that little question includes tons of optimization work. Hogwarts Legacy for instance released on Switch around nine months after its preliminary launch on PC. Even then, the required optimizations make the Swap model nearly a special recreation.

From Digital Foundry’s video on the Hogwarts Legacy change port, exhibiting loading screens added on the Swap model. Some loading screens final for over 40 seconds

Nonetheless, it’s a surprise that builders managed to get video games like Hogwarts Legacy engaged on the change in any respect. I’m wondering if related optimization efforts could possibly be carried out to make fashionable video games accessible to a wider viewers. A variety of avid gamers don’t have infinite budgets, and could possibly be utilizing built-in graphics on outdated CPUs. Swap-like optimizations may make AAA video games playable on these platforms.

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