Wednesday, April 28, 2021

Virtual Workshop: Conceptual Understanding of Deep Learning (May 17th 9am-4pm PST)

** Nuit Blanche is now on Twitter: @NuitBlog **


Just got an email from Rina Panigrahy

Hi Igor,

I am an algorithms researcher at Google (http://theory.stanford.edu/~rinap) and I am organizing this workshop on "Conceptual Understanding of Deep Learning" (details below). It's trying to understand the Brain/Mind as an algorithm from a mathematical/theoretical perspective. I believe that a mathematical/algorithmic approach for understanding the Mind is crucial and very much missing. I'd appreciate any help I can get with advertising this on your blog/mailing-lists/twitter.

Best,
Rina

Here is the invite:

Please join us for a virtual Google workshop on “Conceptual Understanding of Deep Learning

When: May 17th 9am-4pm PST.

Goal: How does the Brain/Mind (perhaps even an artificial one) work at an algorithmic level? While deep learning has produced tremendous technological strides in recent decades, there is an unsettling feeling of a lack of “conceptual” understanding of why it works and to what extent it will work in the current form. The goal of the workshop is to bring together theorists and practitioners to develop an understanding of the right algorithmic view of deep learning, characterizing the class of functions that can be learned, coming up with the right learning architecture that may (provably) learn multiple functions, concepts and remember them over time as humans do, theoretical understanding of language, logic, RL, meta learning and lifelong learning.

The speakers and panelists include Turing award winners Geoffrey Hinton, Leslie Valiant, and Godel Prize winner Christos Papadimitriou (full-details).

Panel Discussion: There will also be a panel discussion on the fundamental question of “Is there a mathematical model for the Mind?”. We will explore basic questions such as “Is there a provable algorithm that captures the essential capabilities of the mind?”, “How do we remember complex phenomena?”, “How is a knowledge graph created automatically?”, “How do we learn new concepts, function and action hierarchies over time?” and “Why do human decisions seem so interpretable?”

Twitter: #ConceptualDLWorkshop.
Please help advertise on mailing-lists/blog-posts and Retweet.


Hope to see you there!




 
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Tuesday, April 27, 2021

Randomized Algorithms for Scientific Computing (RASC)

** Nuit Blanche is now on Twitter: @NuitBlog **

At LightOn, we build photonic hardware that performs random projections and it is nice to find a source of materials on the subject in one document. Here is a report comprehensively presenting how randomized algorithms are key to the future of computing:


Randomized Algorithms for Scientific Computing (RASC) by Aydin Buluc, Tamara G. Kolda, Stefan M. Wild, Mihai Anitescu, Anthony DeGennaro, John Jakeman, Chandrika Kamath, Ramakrishnan (Ramki)Kannan, Miles E. Lopes, Per-Gunnar Martinsson, Kary Myers, Jelani Nelson, Juan M. Restrepo, C. Seshadhri, Draguna Vrabie, Brendt Wohlberg, Stephen J. Wright, Chao Yang, Peter Zwart

Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. This report summarizes the outcomes of that workshop, "Randomized Algorithms for Scientific Computing (RASC)," held virtually across four days in December 2020 and January 2021.

 
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Tuesday, April 06, 2021

The $1,000 GPT-3

** Nuit Blanche is now on Twitter: @NuitBlog **

Progress usually comes from a steady technology bootstrap…until it doesn’t.

Take for instance the race for the $1,000 genome that started in the early 2000s. Initially, sequencing the human genome meant a race between the well-funded public and private sectors but more importantly, the resources for the first breakthrough ended up costing upwards of $450M. Yet despite all the economic promise of genome sequencing, had Moore’s law been applied, sequencing one full genome would still cost $100,000 today. However, once the goal became clearer to everyone, a diversity of technologies and challengers emerged. This intense competition eventually yielded a growth faster than Moore’s Law. The main takeaway is that one cannot rely on the steady progress of one specific technology alone to commoditize tools.



Figure from NIH “Facts sheets about genomics: The cost of Sequencing a Human Genome”, Dec 7th, 2020.

What does this have to do with the current state of silicon computing and the new demand for Large Language Models (LLMs)? Everything if you ask us and here is how.

Less than a year into existence, Large Language Models like GPT-3 have already spawned a new generation of startups built on the ability of the model to respond to requests for which it was not trained. More importantly for us, hardware manufacturers are positing that one or several customers will be willing to put a billion dollars on the table to train an even larger model in the coming years.

Interestingly, much like the mass industrialization in the 1930s, the good folks at OpenAI are sketching new scaling laws for the industrialization of these larger models.

The sad truth is that extrapolating their findings to the training of a 10 Trillion parameters model involves a supercomputer running continuously for two decades. The minimum capital expenditure of this adventure is estimated in the realm of several hundreds of million dollars.

Much like what happened in sequencing, while silicon improvement and architecture may achieve speedups in the following years, it is fair to say that, even with Moore’s law, no foreseeable technology can reasonably train a fully scaled-up GPT-4 and grab the economic value associated with it.



Rebooting silicon with a different physics, light, and NvNs

For a real breakthrough to occur, much like what happened in the sequencing story, different technologies need to be jointly optimized. In our case, this means performing co-design with new hardware and physics but also going rogue on full programmability.

LightOn’s photonic hardware can produce massively parallel matrix-vector multiplications with an equivalent of 2 trillion parameters “for free”: this is about one-fifth of the number of parameters needed for GPT-4. Next comes revisiting the programmability. Current LightOn’s technology keeps these weights fixed by design. Co-design means finding the algorithms for which CPUs and GPUs can perform some of the most intelligent computations and how LightOn’s massive Non-von Neumann (NvN) hardware can do the heavy lifting. We already published how we are replacing backpropagation, the workhorse of Deep Learning, with an algorithm that unleashes the full potential of our hardware in distributed training. We are also working similarly on an inference step that will take full advantage of the massive number of parameters at our disposal. This involved effort relies in a heavy part thanks to our access to ½ million GPU hours on some of France’s and Europe’s largest supercomputers.

And this is just the beginning. There is a vast untapped potential for repurposing large swaths of optical technologies directed primarily for entertainment and telecommunication into computing.

The road towards a $1,000 GPT-3

Based on the GPT-3 training cost estimates, achieving a $1,000 GPT-3 requires four orders of magnitude improvements. Much like what occurred in 2007 with the genome sequencing revolution, Moore’s law may take care of the first two orders of magnitude in the coming decade but the next two rely on an outburst of new efficient technologies — hardware and algorithms. It just so happens that GPT-3 has close to 100 layers, so achieving two orders of magnitude savings may arise faster than you can imagine. Stay tuned!

Igor Carron is the CEO and co-founder at LightOn


 
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Wednesday, March 24, 2021

Computing with Light: How LightOn intends to unlock Transformative AI

I gave a talk at #mathia2021 conference on March 9th, 2021 where I drew a parallel between the scaling laws that enabled industrialization in the 1920's and the new scaling laws in AI of the 2020's. AI is at its infancy and it needs to have guiding principles (as embedded in these empirical laws) and it also needs to develop new hardware. I showed how, in this context, LightOn can help unlock Transformative AI. Enjoy!



All these other presentations by Yann LeCun, Kathryn Hess, Michael Jordan, Emmanuel Candès and others can be found in this collection of videos on Vimeo. Let me note that Michael made a similar argument as mine where we think of current stage of AI at its infancy in terms of industrialization. 





 
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Monday, March 08, 2021

Unveiling LightOn Appliance

Today is a big day at LightOn as we unveil a hardware product, the Appliance, the world's first commercially available photonic co-processor for AI and HPC

If interested pre-ordering information is here: http://lighton.ai/lighton-appliance 

We have had a few of these optical processing units in our own LightOn Cloud for the past two years and just retired one after more than 800 days working full time.  



Here is the press release

The future is now! 


Leasing starts at 1900€/month or about US$2250/month 






 
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Thursday, March 04, 2021

Video: LightOn unlocks Transformative AI

In the coming days, we'll be making another announcement but I wanted to first share a video we did recently. At LightOn, we don't build photonic computing hardware because it's fancy or cool (even though, it is cool) but because computing hardware is hitting the limits. I know what some say about Moore's law not being dead but the recent focus on Transformers and their attendant scaling laws makes it obvious that in order for more people to have access to these models, we need a new computing paradigm. Indeed not everyone can afford to spend a billion dollars in training these models. As Azeem was recently pointing out in one of his newsletters, this is how bad things will become:
The amazing thing is that we can start to compare the cost of training single AI models with the cost of building the physical fabs that make chips. TSMC’s state-of-the-art 3nm fab will run to around $20bn when it is completed in two years. A fab like this may be competitive for 5-7 years, which means it’ll need to churn out $7-8m worth of chips every day before it pays back.

And so at LightOn, we think that a combination of algorithms and (cool) hardware as the only pathway forward for computing large-scale AI. The video is right here, enjoy!







 
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Tuesday, December 29, 2020

The Awesome Implicit Neural Representations Highly Technical Reference Page

** Nuit Blanche is now on Twitter: @NuitBlog ** 

Here is a new curated page on the topic of Implicit Neural Representations aptly called Awesome Implicit Neural Representations. It is curated by Vincent Sitzmann (@vincesitzmann) and has been added to the Highly Technical Reference Page:





From the page:

A curated list of resources on implicit neural representations, inspired by awesome-computer-vision. Work-in-progress.

This list does not aim to be exhaustive, as implicit neural representations are a rapidly evolving & growing research field with hundreds of papers to date.

Instead, this list aims to list papers introducing key concepts & foundations of implicit neural representations across applications. It's a great reading list if you want to get started in this area!

For most papers, there is a short summary of the most important contributions.

Disclosure: I am an author on the following papers:

What are implicit neural representations?

Implicit Neural Representations (sometimes also referred to coordinate-based representations) are a novel way to parameterize signals of all kinds. Conventional signal representations are usually discrete - for instance, images are discrete grids of pixels, audio signals are discrete samples of amplitudes, and 3D shapes are usually parameterized as grids of voxels, point clouds, or meshes. In contrast, Implicit Neural Representations parameterize a signal as a continuous function that maps the domain of the signal (i.e., a coordinate, such as a pixel coordinate for an image) to whatever is at that coordinate (for an image, an R,G,B color). Of course, these functions are usually not analytically tractable - it is impossible to "write down" the function that parameterizes a natural image as a mathematical formula. Implicit Neural Representations thus approximate that function via a neural network.

Why are they interesting?

Implicit Neural Representations have several benefits: First, they are not coupled to spatial resolution anymore, the way, for instance, an image is coupled to the number of pixels. This is because they are continuous functions! Thus, the memory required to parameterize the signal is independent of spatial resolution, and only scales with the complexity of the underyling signal. Another corollary of this is that implicit representations have "infinite resolution" - they can be sampled at arbitrary spatial resolutions.

This is immediately useful for a number of applications, such as super-resolution, or in parameterizing signals in 3D and higher dimensions, where memory requirements grow intractably fast with spatial resolution.

However, in the future, the key promise of implicit neural representations lie in algorithms that directly operate in the space of these representations. In other words: What's the "convolutional neural network" equivalent of a neural network operating on images represented by implicit representations? Questions like these offer a path towards a class of algorithms that are independent of spatial resolution!..........





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Monday, December 21, 2020

Hardware Beyond Backpropagation: a Photonic Co-Processor for Direct Feedback Alignment

** Nuit Blanche is now on Twitter: @NuitBlog ** 

We presented this work at the Beyond Backpropagation workshop at NeurIPS. A great conjunction between computational hardware and algorithm! 




The scaling hypothesis motivates the expansion of models past trillions of parameters as a path towards better performance. Recent significant developments, such as GPT-3, have been driven by this conjecture. However, as models scale-up, training them efficiently with backpropagation becomes difficult. Because model, pipeline, and data parallelism distribute parameters and gradients over compute nodes, communication is challenging to orchestrate: this is a bottleneck to further scaling. In this work, we argue that alternative training methods can mitigate these issues, and can inform the design of extreme-scale training hardware. Indeed, using a synaptically asymmetric method with a parallelizable backward pass, such as Direct Feedback Alignement, communication needs are drastically reduced. We present a photonic accelerator for Direct Feedback Alignment, able to compute random projections with trillions of parameters. We demonstrate our system on benchmark tasks, using both fully-connected and graph convolutional networks. Our hardware is the first architecture-agnostic photonic co-processor for training neural networks. This is a significant step towards building scalable hardware, able to go beyond backpropagation, and opening new avenues for deep learning.



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Saturday, December 19, 2020

Diffraction-unlimited imaging based on conventional optical devices

** Nuit Blanche is now on Twitter: @NuitBlog ** 


Aurélien sent me an email back in October and we are now in December! Time flies.
Dear Igor,


I hope things are well.

I have been following your NuitBlanche blog for quite a few years. It would thus be great for us if you consider a recent paper of ours to appear in your blog, entitled “Diffraction-unlimited imaging based on conventional optical devices”. This paper has been published in Optics Express this year and its link is: https://www.osapublishing.org/oe/abstract.cfm?uri=oe-28-8-11243

This manuscript proposes a new imaging paradigm for objects that are too far away to be illuminated or accessed, which allows them to be resolved beyond the limit of diffraction---which is thus distinct from the microscopy setting. Our concept involves an easy-to-implement acquisition procedure where a spatial light modulator (SLM) is placed some distance from a conventional optical device. After acquisition of a sequence of images for different SLM patterns, the object is reconstructed numerically. The key novelty of our acquisition approach is to ensure that the SLM modulates light before information is lost due to diffraction.

Feel free to let us know what you think, and happy to provide more information/pictures if needed. Thanks a lot for your time and consideration!


Best regards,

Aurélien Bourquard

Thank you Aurélien


 Here is the paper's abstract:




We propose a computational paradigm where off-the-shelf optical devices can be used to image objects in a scene well beyond their native optical resolution. By design, our approach is generic, does not require active illumination, and is applicable to several types of optical devices. It only requires the placement of a spatial light modulator some distance from the optical system. In this paper, we first introduce the acquisition strategy together with the reconstruction framework. We then conduct practical experiments with a webcam that confirm that this approach can image objects with substantially enhanced spatial resolution compared to the performance of the native optical device. We finally discuss potential applications, current limitations, and future research directions.

I note that Aurélien has also published some exciting research on Differential Imaging Forensics. His co-author Nicolas has also some interesting work on Single Pixel cameras.




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Wednesday, December 09, 2020

LightOn at #NeurIPS2020

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I posted the following on LightOn's Blog.




We live in interesting times!

A combination of post-Moore’s law era and the advent of very large ML models require all of us to think up new approaches to computing hardware and AI algorithms at the same time. LightOn is one of the few (20) companies in the world publishing in both AI and hardware venues to engage both communities into thinking how theories and workflows may eventually be transformed by the photonic technology we develop.

This year, thanks to the awesome Machine Learning team at LightOn, we have two accepted papers at NeurIPS, the AI flagship conference, and have five papers in its“Beyond Backpropagation” satellite workshop that will take place on Saturday. This is significant on many levels, not the least being that these papers have been nurtured and spearheaded by two Ph.D. students (Ruben Ohana and Julien Launay) who are doing their thesis as LightOn engineers.

Here is the list of the different papers accepted at NeurIPS this year that involved LightOn members:


And at the NeurIPS Beyond Backpropagation workshop taking place on Saturday, December 12:


  • Hardware Beyond Backpropagation: a Photonic Co-Processor for Direct Feedback Alignment, Julien Launay, Iacopo Poli, Kilian Muller, Igor Carron, Laurent Daudet, Florent Krzakala, Sylvain Gigan
  • Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures, Julien Launay, François Boniface, Iacopo Poli, Florent Krzakala (Presenter: Julien Launay).
  • Ignorance is Bliss: Adversarial Robustness by Design through Analog Computing and Synaptic Asymmetry, Alessandro Cappelli, Ruben Ohana, Julien Launay, Iacopo Poli, Florent Krzakala (Presenter: Alessandro Cappelli). We had a blog post on this recently.
  • Align, then Select: Analysing the Learning Dynamics of Feedback Alignment, Maria Refinetti, Stéphane d’Ascoli, Ruben Ohana, Sebastian Goldt paper (Presenter: Ruben Ohana).
  • How and When does Feedback Alignment Work, Stéphane d’Ascoli, Maria Refinetti, Ruben Ohana, Sebastian Goldt. paper (Presenter: Ruben Ohana)

Some of these presentations are given in French at the “Déjeuners virtuels de NeurIPS”


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