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> Yes, I totally agree. Yann LeCun, Geoff Hinton, Jurgen Schmidhuber and others did unpopular work for a long time. ... > Until then, I'll be ... rolling my e

> Yes, I totally agree. Yann LeCun, Geoff Hinton, Jurgen Schmidhuber and others did unpopular work for a long time. ... > Until then, I'll be ... rolling my eyes at brain analogies. Maybe you don't realize this, but these guys made more brain analogies than you can count over the same period to which you attribute their greatness. Meanwhile, they were attacked year after year by state-of-the-art land grabbers saying the same things you just did. > isn't being presented as basic research on a risky hypothesis. It is basic research, but it's not a risky hypothesis. Existing neuromorphic computers achieve 10^14 ops/s at 20 W. Thats 5 Tops/Watt. The best GPUs currently achieve less than 200 Gops/Watt. Where is the risk in saying that a man-made neuromorphic chip can achieve more per dollar than a GPU. There is no risk, and suggesting that this field is somehow has too much risk for advances to be celebrated is absolutely crazy.

deepnotderp 10y ago
Non-neuromorphic (analog) deep learning chip startup here. We're forecasting AT LEAST ~50 TOPS/watt for inference.
- Russell91 10y ago
  Sure - I guess it's productive for me to answer why this doesn't disagree with my comment. By the time you get the software to hook up that kind of low bit precision (READ: neuromorphic) compute performance with extreme communication-minimizing strategies (READ: neuromorphic), which will invariable require compute colocated, persistent storage (READ: neuromorphic) in any type of general AI application, you're not exactly making the argument that neuromorphic chips are a bad idea. We literally have to start taking neuromorphic to mean some silly semantics like "exactly like the brain in every possible way" in order to disagree with it. Edit: also, to ground this discussion, there are extremely concrete reason why current neural net architectures will NOT work with the above optimizations. That's the primary motivation for talking about "neuromorphic", or any other synonym you want to coin, as fundamentally different hardware. AI software ppl need to have a term for hardware of the future, which simply won't be capable of running AlexNet well at all, in the same way that a GPU can't run CPU code well. I think the term "neuromorphic" to describe this hardware is as productive as any.
  - p1esk 10y ago
    Which existing neuromorphic computers achieve 10^14 ops/s at 20 W? If you compare them to GPUs, those "ops" better be FP32 or at least FP16. Also, you forgot to tell us what is that "extremely concrete reason why current neural net architectures will NOT work with the above optimizations".
    - Russell91 10y ago
      >Which existing neuromorphic computers achieve 10^14 ops/s at 20 W? If you compare them to GPUs, those "ops" better be FP32 or at least FP16. The comparison is of 3 bit neuromorphic synaptic ops against FP8 pascal ops. That factor is important (as it means that the neuromorphic ops are less useful), but it turns out to be dwarfed by the answer to your second question: > Also, you forgot to tell us what is that "extremely concrete reason why current neural net architectures will NOT work with the above optimizations". this is rather difficult to justify in this margin. But the idea is that proposals such as those above (50 Tops) tend to be optimistic on the efficiency of the raw compute ops. But these proposals really don't have much to say about the costs of communication (e.g. reading from memory, transmitting along wires, storing in registers, using buses, etc.). It turns out that if you don't have good ways to reduce these costs directly (and there are some, such as changing out registers for SRAMs, but nothing like the 100x speedup from analog computing), you have to just change the ratio of ops / bit*mm of communication per second. There are lots of easy ways to do that (e.g. just spin your ops over and over on the same data), but the real question is how to get useful intelligence out of your compute when it is data starved. This is an open question, and (sadly), very few ppl are working on it, compared to say low-bit-precision neural nets. But I predict this sentiment will be changed over the next few years. Edit for below: no one is suggesting 50 Top/w hardware running alex net software to my knowledge (though would love to hear what they are proposing to run at that efficiency) . Nvidia among others are squeezing efficiency for cv applications with current software, but this comes at the cost of generality (it's unlike the communication tradeoffs they're making on that chip will make sense for generic AI research), and further improvements will rely on broader software changes, esp revolving around reduced communication. There are a lot of interesting ways to reduce communication without sacrificing performance, such as using smaller matrix sizes, which would reverse the state of the art trends.

deepnotderp 10y ago

Non-neuromorphic (analog) deep learning chip startup here. We're forecasting AT LEAST ~50 TOPS/watt for inference.

Russell91 10y ago
Sure - I guess it's productive for me to answer why this doesn't disagree with my comment. By the time you get the software to hook up that kind of low bit precision (READ: neuromorphic) compute performance with extreme communication-minimizing strategies (READ: neuromorphic), which will invariable require compute colocated, persistent storage (READ: neuromorphic) in any type of general AI application, you're not exactly making the argument that neuromorphic chips are a bad idea. We literally have to start taking neuromorphic to mean some silly semantics like "exactly like the brain in every possible way" in order to disagree with it. Edit: also, to ground this discussion, there are extremely concrete reason why current neural net architectures will NOT work with the above optimizations. That's the primary motivation for talking about "neuromorphic", or any other synonym you want to coin, as fundamentally different hardware. AI software ppl need to have a term for hardware of the future, which simply won't be capable of running AlexNet well at all, in the same way that a GPU can't run CPU code well. I think the term "neuromorphic" to describe this hardware is as productive as any.
- p1esk 10y ago
  Which existing neuromorphic computers achieve 10^14 ops/s at 20 W? If you compare them to GPUs, those "ops" better be FP32 or at least FP16. Also, you forgot to tell us what is that "extremely concrete reason why current neural net architectures will NOT work with the above optimizations".
  - Russell91 10y ago
    >Which existing neuromorphic computers achieve 10^14 ops/s at 20 W? If you compare them to GPUs, those "ops" better be FP32 or at least FP16. The comparison is of 3 bit neuromorphic synaptic ops against FP8 pascal ops. That factor is important (as it means that the neuromorphic ops are less useful), but it turns out to be dwarfed by the answer to your second question: > Also, you forgot to tell us what is that "extremely concrete reason why current neural net architectures will NOT work with the above optimizations". this is rather difficult to justify in this margin. But the idea is that proposals such as those above (50 Tops) tend to be optimistic on the efficiency of the raw compute ops. But these proposals really don't have much to say about the costs of communication (e.g. reading from memory, transmitting along wires, storing in registers, using buses, etc.). It turns out that if you don't have good ways to reduce these costs directly (and there are some, such as changing out registers for SRAMs, but nothing like the 100x speedup from analog computing), you have to just change the ratio of ops / bit*mm of communication per second. There are lots of easy ways to do that (e.g. just spin your ops over and over on the same data), but the real question is how to get useful intelligence out of your compute when it is data starved. This is an open question, and (sadly), very few ppl are working on it, compared to say low-bit-precision neural nets. But I predict this sentiment will be changed over the next few years. Edit for below: no one is suggesting 50 Top/w hardware running alex net software to my knowledge (though would love to hear what they are proposing to run at that efficiency) . Nvidia among others are squeezing efficiency for cv applications with current software, but this comes at the cost of generality (it's unlike the communication tradeoffs they're making on that chip will make sense for generic AI research), and further improvements will rely on broader software changes, esp revolving around reduced communication. There are a lot of interesting ways to reduce communication without sacrificing performance, such as using smaller matrix sizes, which would reverse the state of the art trends.