High level synthesis hopefully will one day achieve it's said goals of matching the corresponding RTL design flow in terms of output performance and efficiency and more insightful design space
51:55 "re-verticalizing" back to the 1970s, expect to see very specialized stacks again, esp for power control (EV, smart grid) where security has to be ironclad, and standards have to hold across the entire planet, and every country will demand verifiability down to the transistor, and won't accept any assurance even from its allies.
I think though, that the 5-year-old - when asked about the difference between a cat and a dog - would be generating his answer on the fly by diffing a cat and a dog in front of them. If you asked them the difference between a cat and dog without having any pictures nearby, it's a much tougher question to answer. The most widely-adopted ML models are still given away for free (think google picture classification and translation which is at the level of a 12-year old with a good dictionary). Also if you show a cat to a picture classifier you get an answer of 0.83% cat and 0.22% dog, which is a little bit sketchy. I am skeptical about how soon ML applications will go beyond experimental / free and reach profitability.
8:30 Yup I wrote 6502 assembly code without any assembler even, "POKE"ing it into RAM and then saving the memory to tape. Not small programs either. 2k long.
43:09 "C like" scripting languages are the worst of all worlds. LISP derivatives with tail recursion will always mop the floor with them on any math. C++ or D should also, unless the coder is a dummy (many are) and can't use static binding. Stop teaching Java and Python. JavaScript is so much better...and the cloud languages (Haskell, Scala, etc) exist for good reason.
High level synthesis hopefully will one day achieve it's said goals of matching the corresponding RTL design flow in terms of output performance and efficiency and more insightful design space
can you expand on this
I think that's what he said at around 46:26 that it has been tried and not worked as a viable solution.
This is already true today. Units on NVIDIA GPU are designed using HLS.
51:55 "re-verticalizing" back to the 1970s, expect to see very specialized stacks again, esp for power control (EV, smart grid) where security has to be ironclad, and standards have to hold across the entire planet, and every country will demand verifiability down to the transistor, and won't accept any assurance even from its allies.
I think though, that the 5-year-old - when asked about the difference between a cat and a dog - would be generating his answer on the fly by diffing a cat and a dog in front of them. If you asked them the difference between a cat and dog without having any pictures nearby, it's a much tougher question to answer.
The most widely-adopted ML models are still given away for free (think google picture classification and translation which is at the level of a 12-year old with a good dictionary). Also if you show a cat to a picture classifier you get an answer of 0.83% cat and 0.22% dog, which is a little bit sketchy. I am skeptical about how soon ML applications will go beyond experimental / free and reach profitability.
8:30 Yup I wrote 6502 assembly code without any assembler even, "POKE"ing it into RAM and then saving the memory to tape.
Not small programs either. 2k long.
Is this a Computer Systems course?
you may be a reason CA advancements are at a halt. jk.
38:15 Spectre just showed what garbage x86 is. Anyone who relied on Intel "enclaves" should sue for every new bug they find every month.
43:09 "C like" scripting languages are the worst of all worlds. LISP derivatives with tail recursion will always mop the floor with them on any math. C++ or D should also, unless the coder is a dummy (many are) and can't use static binding.
Stop teaching Java and Python. JavaScript is so much better...and the cloud languages (Haskell, Scala, etc) exist for good reason.