rec.games.chess.computer,rec.games.chess.misc,comp.arch.embedded,alt.comp.h= ardware.pc- homebuilt
Can anybody help me figure out how fast (what Elo rating) modern cell phone and mobile phone hardware play chess? Go to the SSDF rating list
Which shows that an XScale 400Mhz machine plays like a Pentium IV class machine from around 2003. I think XScale is used in some older (few years ago) Nokia cell phones.
But what about more modern cell phone hardware, like the ones that use Qualcomm's SnapDraggon chip?
From what I can tell, the following rule of thumb is correct.
Snapdragon (single core) and Marvell's Armada 500/600, both based on ARMv7 implementations, are roughly equal to Intel's Atom. Intel's Atom is roughly equal to a 2003-2004 vintage Celeron. So using SSDF you can do the math and see what a mobile phone playing software chess program like Pocket Fritz can do in a cell phone employing these chips. Roughly the programs would have an Elo on the SSDF list of a little above 2660 Elo on the SSDF list, probably close to 2700 to 2750 (dual core Snapdragon cell phones, which come out in late 2011 to
2012, would be at the upper limit).But does anybody have more precise figures? Perhaps based on the number of chess nodes searched per second for various chips embedded in mobile PDAs and cell phones?
RL
From the net...
Anybody
Embedded processors based on the ARM version 7 instruction set architecture (such as TI's OMAP 3 series and Freescale's i.MX51 based on the Cortex-A8 processor, or the Qualcomm Snapdragon and Marvell Armada 500/600 based on custom ARMv7 implementations) offer similar performance to the low end Atom chipsets[dubious =96 discuss] but at roughly one quarter the power consumption, and (like most ARM systems) as a single integrated system on a chip, rather than a two chip solution like the current Atom line. Although the next-generation Atom codenamed "Pineview" should greatly increase its competitiveness in performance/watt, ARM plans to counter the threat with the multi-core capable Cortex-A9 processor as used in Nvidia's Tegra 2, T.I.'s OMAP 4 series, and Qualcomm's next-generation Snapdragon series, among others.