Discussion Apple Silicon SoC thread

[Eug]

M1
5 nm
Unified memory architecture - LP-DDR4
16 billion transistors

8-core CPU

4 high-performance cores
192 KB instruction cache
128 KB data cache
Shared 12 MB L2 cache

4 high-efficiency cores
128 KB instruction cache
64 KB data cache
Shared 4 MB L2 cache
(Apple claims the 4 high-effiency cores alone perform like a dual-core Intel MacBook Air)

8-core iGPU (but there is a 7-core variant, likely with one inactive core)
128 execution units
Up to 24576 concurrent threads
2.6 Teraflops
82 Gigatexels/s
41 gigapixels/s

16-core neural engine
Secure Enclave
USB 4

Products:
$999 ($899 edu) 13" MacBook Air (fanless) - 18 hour video playback battery life
$699 Mac mini (with fan)
$1299 ($1199 edu) 13" MacBook Pro (with fan) - 20 hour video playback battery life

Memory options 8 GB and 16 GB. No 32 GB option (unless you go Intel).

It should be noted that the M1 chip in these three Macs is the same (aside from GPU core number). Basically, Apple is taking the same approach which these chips as they do the iPhones and iPads. Just one SKU (excluding the X variants), which is the same across all iDevices (aside from maybe slight clock speed differences occasionally).

EDIT:



M1 Pro 8-core CPU (6+2), 14-core GPU
M1 Pro 10-core CPU (8+2), 14-core GPU
M1 Pro 10-core CPU (8+2), 16-core GPU
M1 Max 10-core CPU (8+2), 24-core GPU
M1 Max 10-core CPU (8+2), 32-core GPU

M1 Pro and M1 Max discussion here:

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M1 Ultra discussion here:

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M2 discussion here:

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Second Generation 5 nm
Unified memory architecture - LPDDR5, up to 24 GB and 100 GB/s
20 billion transistors

8-core CPU

4 high-performance cores
192 KB instruction cache
128 KB data cache
Shared 16 MB L2 cache

4 high-efficiency cores
128 KB instruction cache
64 KB data cache
Shared 4 MB L2 cache

10-core iGPU (but there is an 8-core variant)
3.6 Teraflops

16-core neural engine
Secure Enclave
USB 4

Hardware acceleration for 8K h.264, h.264, ProRes

M3 Family discussion here:

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M4 Family discussion here:

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M5 Family discussion here:

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[Mopetar]

Theoretically yes, the problem is that these configurations are fixed at packaging time so they would need to stock more SKUs in inventory and risk overproducing one and underproducing another.

It's obviously not something that can be done now, but I can see a push towards implementing the same JIT approach when it comes to semiconductor manufacturing and packaging.

If robotics advances enough over the next decade there's little reason not to move manufacturing of finished products closer to the markets where they'll be sold.

Companies only need to stock the most popular configurations that will sell through quickly enough that there's less risk of overproduction. If anything custom can be made to order and delivered in a week or two most customers will be happy with a slight wait.

There's still the roadblock of longer lead time for manufacturing the silicon. If that takes several months from a wafer start to shipping chiplet, it will struggle to adjust to any rapid shifts in consumer demand.

 

[fkoehler]

Not quite sure where exactly I said chiplets are a perfect technology.
Anyone with sense would understand its an approach that reduces potential die destroying or die wastage.
Nothing is free, and fabrics/etc cost money, but you get numerous benefits from mixing-matching litho processes in a way monlithic simply doesn't.

You're the one seeming to believe its the wrong approach, for.. reasons.
Personally, I'll trust the folks who make their living competing for the cheapest production cost as likely having just a bit more data than myself or anyone else outside the field.