6 ms·

Interesting, here is another link with some additional interesting points: https://www.electronicsweekly.com/news/design/tunnelling-transistor-offers-logic-powe

Interesting, here is another link with some additional interesting points: https://www.electronicsweekly.com/news/design/tunnelling-transistor-offers-logic-power-easy-make-ic-2019-10/ https://www.electronicsweekly.com/news/design/tunnelling-tra... What caught my eye was this tidbit.... "Analogue computing The last trick up SFN’s sleeve is Bizen analogue blocks – op-amps and comparators (in the PDK), and even analogue computing blocks such as a divider – the analogue computing blocks avoiding using logic – and thus the associated ADC and DAC – in certain applications. Using a coarse-fine architecture, Summerland is predicting 1μW 1μs analogue division instead of ~20 instructions and ~100 instruction cycles if logic was employed. This analogue processing capability is being branded IPU for ‘instantaneous processing unit’." Analog computers making a comeback ? you never know.

ajuc 7y ago
I wonder if using this for low-precision floating point math in graphic cards would make sense. Maybe it's a way to do realtime raytracing? Who cares if the results have a little noise? It can be even argued it gives it a special "look".
breck 7y ago
> Analog computers making a comeback ? you never know. For dataflow paradigms like deep neural nets it makes sense. If it's significantly faster and significantly more power efficient, and the intermediate states are largely arbitrary weights anyway, what's the point of digital precision.
- regularfry 7y ago
  It's not the precision, it's avoiding O(n^2) physical connections per layer.
  - breck 7y ago
    That's my point. Swapping digital for analog gives up the ability to know the precise bits of a register but in return you gain a lot in efficiency.
    - regularfry 7y ago
      That's not what I'm getting at. It's not the nodes you need to worry about, it's the edges. If you've got two fully connected 100-neuron layers in a physical circuit, that's 10,000 wires you need to route, and they all want to cross each other. That's why nobody builds physical neural networks.
colechristensen 7y ago
I guess all you would have to do is to make sure the precision was a few dB/bits more accurate than 16/32/64 bit floating point for a certain sigma probability and a lot of people would be perfectly happy with it.
- arcticbull 7y ago
  I wonder if you could use it as an efficient starting point for various different integer division algorithms which rely on a rough starting point. For instance, it could be your X_0 estimate for (1/D) in the Newton-Rhapson method [1]. Or, potentially, as a high-quality first stage input for a successive-approximation approach [2]. This could allow you to get a rough approximation incredibly quickly and opt to refine it further using it as a high-quality input to speed up finding the "true" answer. [1] https://en.wikipedia.org/wiki/Division_algorithm https://en.wikipedia.org/wiki/Division_algorithm [2] http://www.ijiee.org/papers/173-E0005.pdf http://www.ijiee.org/papers/173-E0005.pdf
  - cardiffspaceman 7y ago
    The trade off would be that your A-D converter would have to be fast enough as well as offer enough bits of result to give the iterative algorithm a running start. Digital video cameras would be the class of device we ~all have that does high-samples-per-second conversion. A 4K@60p camera has to do 500Ms/s at least so if you can allow that many approximate results per second then it might work out. There would also have to be a comparably fast DAC to tell the analog circuit what you want to approximate. I think a more-complete computing engine in analog would be a bigger win, because there won't be conversion overheads. Or as was suggested somewhere in this topic, a field-programmable analog array.
marcosdumay 7y ago
Probably not analog computers. But analog circuitry, CPU, and power supply all in one package seems likely.
- arcticbull 7y ago
  Analog computers are incredibly fast for solving some very difficult problems such as integration. I could see, if easy to implement, basically an "analog FPGA" hybrid of digital fabric and analog components for you to piece together.
  - vegetablepotpie 7y ago
    You may be interested to know that FPAAs are a thing that does exist. https://www.anadigm.com/_doc/DS030100-U006.pdf https://www.anadigm.com/_doc/DS030100-U006.pdf
    - arcticbull 7y ago
      That's really cool, thanks for sharing!
  - dreamcompiler 7y ago
    They're also noise-sensitive, hard to calibrate, hard to reprogram, and only suitable for a fairly small range of problems. But nevertheless very cool devices.
    - m0xte 7y ago
      Bloody awful things. I’ve built a few simple analogue PID loops and simple analogue computers over the years and it’s an art and a half. I think the biggest pain I had was before the availability of reasonably stable log converters was trying to use matched transistor pairs to perform log operations. Took three days to work out where an error was creeping in and it was from the thermal offset voltage of the buffer opamp input. The circuit was drifting due to my desk lamp warming it up. The IC was out of spec on drift too! :( I did succeed in producing a four quadrant multiplier in the end but required some unholy things to be done. That and noise and the cost of precision make it almost useless compared to a digital computer now. The rise of direct sampling SDRs confirms this. Crazy dynamic range, excellent noise floor and hardly any analogue up front.
gumby 7y ago
> Analog computers making a comeback ? you never know. Analogue processing may be a superior alternative to GPGPU for implementing neural networks.
- slashblake 7y ago
  Nailed it: https://www.mythic-ai.com/technology/ https://www.mythic-ai.com/technology/
- zozbot234 7y ago
  I dispute this. The problem with analogue processing (besides noise, of course) is that the dynamic range sucks compared to plain old floating point. Fine for inference, not for training. And most of the compute power in NN is spent on training, inference is trivial.
  - emmelaich 7y ago
    Analogue processing has certainly been successful for people, so I wouldn't be too dismissive.
    - krastanov 7y ago
      I would not be certain of the premise in your statement. We do not understand the brain well enough to say whether error correction (which is inherently digital) is utilized. There are some pretty solid clues that some of the processing in the brain is not analog (e.g. the step-like behavior of the activation function of biological neurons). And coming from the other side: If it turns out that the brain is actually an analog computer without anything that can be rephrased as some form of sloppy noisy approximately-digital error correction, this would be incredibly important discovery, completely changing our understanding of theoretical computer science and complexity theory. P-vs-NP or the eventual creation of quantum computers would pale in comparison to the impact of discovering scalable analog computers.
      - emmelaich 7y ago
whatshisface 7y ago
>Analog computers making a comeback ? you never know. The main advantage they're advertising for the analog parts is that you don't need an ADC or DAC. That's not helpful for computers, but it can be very helpful for specialized signal processing parts.

ajuc 7y ago

I wonder if using this for low-precision floating point math in graphic cards would make sense. Maybe it's a way to do realtime raytracing? Who cares if the results have a little noise? It can be even argued it gives it a special "look".

breck 7y ago

> Analog computers making a comeback ? you never know. For dataflow paradigms like deep neural nets it makes sense. If it's significantly faster and significantly more power efficient, and the intermediate states are largely arbitrary weights anyway, what's the point of digital precision.

regularfry 7y ago
It's not the precision, it's avoiding O(n^2) physical connections per layer.
- breck 7y ago
  That's my point. Swapping digital for analog gives up the ability to know the precise bits of a register but in return you gain a lot in efficiency.
  - regularfry 7y ago
    That's not what I'm getting at. It's not the nodes you need to worry about, it's the edges. If you've got two fully connected 100-neuron layers in a physical circuit, that's 10,000 wires you need to route, and they all want to cross each other. That's why nobody builds physical neural networks.

colechristensen 7y ago

I guess all you would have to do is to make sure the precision was a few dB/bits more accurate than 16/32/64 bit floating point for a certain sigma probability and a lot of people would be perfectly happy with it.

arcticbull 7y ago
I wonder if you could use it as an efficient starting point for various different integer division algorithms which rely on a rough starting point. For instance, it could be your X_0 estimate for (1/D) in the Newton-Rhapson method [1]. Or, potentially, as a high-quality first stage input for a successive-approximation approach [2]. This could allow you to get a rough approximation incredibly quickly and opt to refine it further using it as a high-quality input to speed up finding the "true" answer. [1] https://en.wikipedia.org/wiki/Division_algorithm https://en.wikipedia.org/wiki/Division_algorithm [2] http://www.ijiee.org/papers/173-E0005.pdf http://www.ijiee.org/papers/173-E0005.pdf
- cardiffspaceman 7y ago
  The trade off would be that your A-D converter would have to be fast enough as well as offer enough bits of result to give the iterative algorithm a running start. Digital video cameras would be the class of device we ~all have that does high-samples-per-second conversion. A 4K@60p camera has to do 500Ms/s at least so if you can allow that many approximate results per second then it might work out. There would also have to be a comparably fast DAC to tell the analog circuit what you want to approximate. I think a more-complete computing engine in analog would be a bigger win, because there won't be conversion overheads. Or as was suggested somewhere in this topic, a field-programmable analog array.

marcosdumay 7y ago

Probably not analog computers. But analog circuitry, CPU, and power supply all in one package seems likely.

arcticbull 7y ago
Analog computers are incredibly fast for solving some very difficult problems such as integration. I could see, if easy to implement, basically an "analog FPGA" hybrid of digital fabric and analog components for you to piece together.
- vegetablepotpie 7y ago
  You may be interested to know that FPAAs are a thing that does exist. https://www.anadigm.com/_doc/DS030100-U006.pdf https://www.anadigm.com/_doc/DS030100-U006.pdf
  - arcticbull 7y ago
    That's really cool, thanks for sharing!
- dreamcompiler 7y ago
  They're also noise-sensitive, hard to calibrate, hard to reprogram, and only suitable for a fairly small range of problems. But nevertheless very cool devices.
  - m0xte 7y ago
    Bloody awful things. I’ve built a few simple analogue PID loops and simple analogue computers over the years and it’s an art and a half. I think the biggest pain I had was before the availability of reasonably stable log converters was trying to use matched transistor pairs to perform log operations. Took three days to work out where an error was creeping in and it was from the thermal offset voltage of the buffer opamp input. The circuit was drifting due to my desk lamp warming it up. The IC was out of spec on drift too! :( I did succeed in producing a four quadrant multiplier in the end but required some unholy things to be done. That and noise and the cost of precision make it almost useless compared to a digital computer now. The rise of direct sampling SDRs confirms this. Crazy dynamic range, excellent noise floor and hardly any analogue up front.

gumby 7y ago

> Analog computers making a comeback ? you never know. Analogue processing may be a superior alternative to GPGPU for implementing neural networks.

slashblake 7y ago
Nailed it: https://www.mythic-ai.com/technology/ https://www.mythic-ai.com/technology/
zozbot234 7y ago
I dispute this. The problem with analogue processing (besides noise, of course) is that the dynamic range sucks compared to plain old floating point. Fine for inference, not for training. And most of the compute power in NN is spent on training, inference is trivial.
- emmelaich 7y ago
  Analogue processing has certainly been successful for people, so I wouldn't be too dismissive.
  - krastanov 7y ago
    I would not be certain of the premise in your statement. We do not understand the brain well enough to say whether error correction (which is inherently digital) is utilized. There are some pretty solid clues that some of the processing in the brain is not analog (e.g. the step-like behavior of the activation function of biological neurons). And coming from the other side: If it turns out that the brain is actually an analog computer without anything that can be rephrased as some form of sloppy noisy approximately-digital error correction, this would be incredibly important discovery, completely changing our understanding of theoretical computer science and complexity theory. P-vs-NP or the eventual creation of quantum computers would pale in comparison to the impact of discovering scalable analog computers.
    - emmelaich 7y ago

whatshisface 7y ago

>Analog computers making a comeback ? you never know. The main advantage they're advertising for the analog parts is that you don't need an ADC or DAC. That's not helpful for computers, but it can be very helpful for specialized signal processing parts.