Arria 10: <a href="http:&#x2F;&#x2F;www.nextplatform.com&#x2F;2015&#x2F;08&#x2F;27&#x2F;microsoft-extends-fpga-reach-from-bing-to-deep-learning&#x2F;" rel="nofollow">http:&#x2F;&#x2F;www.nextplatform.com&#x2F;2015&#x2F;08&#x2F;27&#x2F;microsoft-extends-fpg...</a><p>600 images&#x2F;s inference (highest ever publically reported), projected to be as high as 900 images&#x2F;s.  ~$5000 per FPGA.<p>Compare and contrast with TitanX&#x2F;GTX 980 TI now topping 5000 images&#x2F;s.  GTX 1080 will only be faster than this and $600.<p><a href="https:&#x2F;&#x2F;github.com&#x2F;soumith&#x2F;convnet-benchmarks" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;soumith&#x2F;convnet-benchmarks</a><p>And so far, no FPGA training numbers.  But here are some distributed training numbers for $10Kish Xeon servers:<p><a href="https:&#x2F;&#x2F;software.intel.com&#x2F;en-us&#x2F;articles&#x2F;caffe-training-on-multi-node-distributed-memory-systems-based-on-intel-xeon-processor-e5" rel="nofollow">https:&#x2F;&#x2F;software.intel.com&#x2F;en-us&#x2F;articles&#x2F;caffe-training-on-...</a><p>64 of them train Alexnet in ~5 hours.  A DGX-1 does it in 2.  Don&#x27;t have&#x2F;Can&#x27;t get a $129K DGX-1?  Fine, buy a Big Sur from one of several vendors and throw in 8 GTX 1080s as soon as they&#x27;re out, implement &quot;One Weird Trick&quot;, and you&#x27;ll do it in &lt;3. That ought to run you about $30-$35K versus &gt;$600K for those 64 Xeon servers.<p>FPGAs OTOH are fantastic at low memory bandwidth embarrassingly parallel computation like bitcoin mining.  Deep Learning is not such a domain.