DISCOVER WHAT’SPOSSIBLE WITH
NEXT-GENERATION AI

”This pre-AI to post-AI transition that the world is going
through is going to be as big, if not bigger, than theinternet. The magnitude of changes that the internet
brought to every industry will pale in comparison to
the transformation driven by AI.

RODRIGO LIANG, CO-FOUNDER AND CEO

CONTENTS

S ECTIO N 1

SE C T I O N 2

SE C T I ON 3

S ECTION 4S ECTION 5

SECTI ON 6

Paving the Way to
Next-Gen Innovation

The Advent ofDeep Learning

Roadblocks
to Progress

A Flexible New
Design Architecture

From Zero toState-of-the-ArtAI Overnight

Customer
Testimonials +Conclusion

4710151923

Discover What’s Possible With Next-Generation AI3

S ECTIO N 1

PAVING THE WAY TO NEXT-GEN INNOVATIONSE C T ION 1

Disruptive Innovations vsSustaining Innovations
History is the story of what Harvard Business School
professor Clayton Christensen dubbed “Sustaininginnovations” and “Disruptive innovations.”
From carriages to automobiles, floppy disks tohard drives, mainframes to handheld devices,
disruptive innovations have been the catalyst
for societal flourishing.
However, most companies produce sustaining
innovations, developing incrementally better
products that yield short-term profits at the riskof being left behind by more nimble upstarts.

Discover What’s Possible With Next-Generation AI

5SE C T ION 1

Technological Changes
Come in a Blink of an Eye

Over50Technological change comes fast, displacing
existing incumbents who aren’t prepared. By the%

Over

80

Companies in the Fortune 500 in%
2000 that no longer exist today.

time companies realize their predicament andstart playing catch-up, new leaders are bornand established players risk extinction.
Case in point, over 50% of the companies in theFortune 500 in 2000 no longer exist today. And over80% of companies who have tried to adopt digitaltransformation have failed.
The determining factor was whether companies

Over50%

Over80%Companies that have tried toadopt digital transformation
but have failed.

embraced change and were early adopters of
new technology, or just wait-and-see bystanders.

Sources: (Top chart) Business Apocalypse: Fifty-two Percent of Fortune 500 Companies from the Year 2000 Are Extinct https://ryanberman.com/
glossary/business-apocalypse/ (Bottom chart) Couchbase: Digital Transformation Projects Continue to Be at Risk, Couchbase Research Finds
https://www.couchbase.com/press-releases/digital-transformation-projects-continue-to-be-at-risk-couchbase-research-finds

Discover What’s Possible With Next-Generation AI6

S ECTIO N 2

THE ADVENT OF DEEP LEARNINGSE C T ION 2

Deploying Neural Networks
In 2012, researchers took decades-old techniquescalled neural networks and demonstrated thatthey could train deeper and more efficiently,
utilizing hardware once reserved for computer
graphics, the GPU.
Beating all previous benchmarks on thefamous ImageNet dataset, neural networks
now encapsulated under deep learning havebecome ubiquitous as the state-of-the-art inareas like Computer Vision, Natural Language
Processing, and Recommender Systems.
From chatbots that can converse on the finer
points of finance, to cancer detection in MRIs,
to self-driving cars, applications that were once
considered science projects are now seeing thelight of day in real-world deployments.

Discover What’s Possible With Next-Generation AI8

SE C T ION 2

The introduction of billion-parameter models, such as
GPT-3, have redefined what we thought possible andsurpassed human performance in many domains.

Challenges to Innovation:

However, the rise of these more powerful models andexponentially growing datasets have strained legacy
systems and have necessitated new software andPhysical space constraints

hardware architectures. While GPUs ushered in this firstwave of deep learning advancement, we are alreadypushing up against the hardware’s limitations.
To stay ahead of the innovation curve, the solution
to date has been to create specialized teams of

Spiraling costs

deep learning experts to run training workloads on
thousands of GPU cores.
This approach creates a whole set of challenges, suchas physical space constraints, spiraling costs, andspecialized knowledge that together exclude all but

Specialized knowledge

the largest enterprises from reaping the rewards of AI.

Discover What’s Possible With Next-Generation AI

9S ECTIO N 3

ROADBLOCKS TO PROGRESS”
If you look at those who can develop these systems, they’rein the hands of the few. The large companies that have thedata, the computation, and the talent to develop thesesorts of algorithms have used these systems to become themost valuable companies in the world—Google, Apple,Amazon, Facebook, and the like.
KUNLE OLUKOTUN, CO-FOUNDER AND CHIEF TECHNOLOGISTSE C T ION 3

Obstacles in Putting AI Strategies
Into Action
Companies of all sizes face seemingly insurmountableobstacles when it comes to putting AI strategies intoaction. To start, AI and ML talent is expensive andhard to find, leaving most organizations with criticalskills gaps.
Once an organization manages to procure thetalent they require, they’ll need expertise in different
computing architectures to build integrated systems
and make models performant, meeting SLAs andother requirements.
Lastly, organizations find it difficult to stay up todate on the right solution and keep it relevantwith constantly changing data.

Discover What’s Possible With Next-Generation AI

12

SE C T ION 3

Roadblocks to AI
1.

2.
Talent scarcity in AI/ML

3.
Volume of Data

The DIY Approach

Leveraging popular deep learning

The demand for compute can no longer

When it comes to AI, going with theframeworks and software ecosystems

keep up with today’s massive amounts“build your own” model can take 18-24

requires technical expertise that isof data.

months, delaying access to insights andexpensive and in short supply.

Data scientists are constantly looking for

Where once building and deploying deep

workarounds either by decreasing thelearning models was limited to hyperscale

resolution on their datasets, working withenterprises that could afford to hiresubsamples, or deliberately selecting

hundreds of skilled technical developers,less powerful models trading accuracy

today’s organizations want the same AI

for viability.

superpowers as hyperscalers, with only afraction of the budget.

These trade-offs compromise the quality ofend results and prevent businesses from fully

new revenue streams, with no guaranteeof success. In fact, 85% of AI projects delivererroneous outcomes, presenting daunting
odds and costly risk on the hard road todelivering real value.*
Optimizing new systems and keeping
models up to date requires access toever-evolving and scarce AI expertise.

realizing the promise of AI.

*Source: Gartner: Gartner Says Nearly Half of CIOs Are Planning to Deploy Artificial Intelligence https://www.gartner.com/en/newsroom/press-releases/2018-02-13-gartner-says-nearly-half-of-cios-are-planning-to-deploy-artificial-intelligence

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13

SE C T ION 3

There is a Better Way
To solve the many obstacles and roadblocks,
SambaNova Systems has developed an
innovative hardware and software platformthat provides access to state-of-the-art
machine learning solutions to large andsmall organizations alike.
Built by one of the world’s leading chipteams and software leaders from acrossacademia and industry, SambaNova’s
DataScale® and Dataflow-as-a-Service™
enable digital transformation throughdisruptive AI innovation.

Discover What’s Possible With Next-Generation AI14S ECTIO N 4

A FLEXIBLE NEW DESIGN ARCHITECTURE

SE C T ION 4

Reconfigurable DataflowArchitectureWith its flagship DataScale product, SambaNova
delivers a complete, full-stack solution thatincorporates innovations at all layers, including
algorithms, compilers, system architecture, andstate-of-the-art silicon—the Reconfigurable
Dataflow Architecture™ (RDA).
DataScale provides a flexible execution model thatpipelines operations and decomposes dataflow
graphs into parallel patterns—key parts of all
deep learning model architectures—resulting inacceleration in model training and inference, thusreducing time to deployment.
RDA enables programmable data access patterns
and minimizes excess data movement found in
fixed, core-based, instruction set architectures(ISAs). Instead of fixed traditional architectures, RDA
is programmed specifically for each model resulting
in a highly optimized, application-specific accelerator.

Discover What’s Possible With Next-Generation AI

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SE C T ION 4

Future-Proof Your Infrastructure
for Next-Gen AI Workloads
DataScale helps users achieve unparalleledefficiency and performance across a broad
range of applications. Built with open standards
and interfaces, it is designed to avoid disruption
and seamlessly integrate into existing on-prem
infrastructures or cloud-based environments.
SambaNova’s Reconfigurable Dataflow Unit (RDU)
is the industry’s next-generation processor purposebuilt to support AI/ML workloads. RDUs are designed
to allow data to flow through the processor as itshould—freely and without bottlenecks.

”

For the first time, there is
a commercially-available
alternative to NVIDIA’s GPUfor deep learning.

RDUs eliminate constant data caching andexcess data movement inherent to today’s corebased architectures. This unlocks significant siliconutilization to unleash more compute power than

RODRIGO LIANG, CO-FOUNDER AND CEO

any other solution available today.

Discover What’s Possible With Next-Generation AI17

SE C T ION 4

The Complete Software Stack
The complete software stack—SambaFlow—isThe result of all these innovations is aTo borrow from Henry Ford, SambaNova

designed to take input from standard machine-

reimagination of what could be done in areas

hasn’t raised a faster horse (GPU-basedlearning frameworks, such as PyTorch andsuch as Computer Vision, Recommendation

solutions), it has built the first car (Dataflow-TensorFlow. It automatically extracts, optimizes,

Systems and Natural Language Processing.

as-a-Service)—redefining transportation (AI)and maps dataflow graphs onto RDUs, allowing

as we know it today.

high performance without the need for low-level
kernel tuning.

User Graph

User Kernel

Dataflow Gra ph Analyzer

Template Compiler

Dataflow Gra phs

Spatial Templates

Runtime

Dataflow Optimi zer, Compiler, & Assembler

Discover What’s Possible With Next-Generation AI18

S ECTIO N 5FROM ZERO TO STATE-OF-THE-ART AI OVERNIGHT”
A new phenomenon has started to emerge in the last
few years: machine learning has begun to eat software.
Machine learning is radically changing how one builds,deploys, and maintains software.
CHRIS RE, CO-FOUNDER

Dataflow-as-as-Service™

SE C T ION 5

Dataflow-as-a-Service

INSIGHTSData

M O DE L S + S E R VICE S

The past decades were defined by imperative
programming, where engineers and scientists
explicitly and meticulously crafted algorithms
by hand. Today, the revolution in computing isdefined by Software 2.0, or dataflow, where data
has become the electricity of the digital age.
Powered by the DataScale hardware engine,

Natural language
processing

ComputervisionRecommendation
systems

SambaNova has released Dataflow-as-a-Service
to provide everyone with access to state-of-the-F u lly ma n a ged a n d s u p p o r t ed

art machine learning models.
With Dataflow-as-a-Service, organizations

E X TE N S IB L E M L S E R VICE S P L AT F OR M

outside of Big Tech can offload complexity tostay focused on their core competencies while
deploying AI solutions with confidence.
Powered by Da t a S ca le ®

DATA

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SE C T ION 5

Augment Your Machine Learning Expertise
A Dataflow-as-a-Service subscription gives companies a path to AI.

FastestEasiest

Guaranteed

Deploy customized solutions with
best-in-class accuracy on yourdata to meet your needs.

Augment your ML expertise. You don’t
need to be a hyperscaler to deploy
world-class machine learning.

Stay current with the latest advancements
seamlessly. Continuous updates ensure yoursolutions stay ahead of the curve.

Scalable infrastructure, methods and talent formachine learning inference and a simple route
to application deployment and production.

Lower-level APIs to train models using
advanced fine-tuning, and zero-shotlearning strategies.

Industry-specific pre-built machinelearning solutions validated by realworld use cases.

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S ECTIO N 6

CUSTOMER TESTIMONIALS + CONCLUSIONSE C T ION 6

Customer Testimonials
Lawrence Livermore National Laboratory”

The support for running multiple models built into the hardware was a significant
aspect of our selection of SambaNova Systems. We’re seeing roughly a 5ximprovement compared to a comparable GPU running the same models.
BRONIS DE SUPINKSI, CTO

Lawrence Livermore National Laboratory (LLNL) uses large-scale systems for various physics simulations. Leveraging AI to achieve significant performance
improvement for scientific applications, LLNL actively explores cognitive simulation using SambaNova’s technology. Data is moved from supercomputers tothe SambaNova system over LLNL’s high-speed network and ingested into its models and integrated into its overall simulation processes.

Discover What’s Possible With Next-Generation AI24SE C T ION 6Customer Testimonials
Argonne National Laboratory”

We’re interested in technology that can train models faster than
individual GPUs, that has more scalability and has more performance,and so the SambaNova DataScale System met that criteria.
RICK STEVENS, ASSOCIATE LABORATORY DIRECTOR

Argonne National Laboratory uses AI for cancer and Covid-19 research. In particular, its AI models are used for drug development to automate multiple partsof the development pipeline. It is also used in cosmology to look for evidence of dark matter and in environmental sensing. The time it takes to build and trainmodels is critical for research. Argonne sought out technology that was faster than individual GPUs, had more scalability, and could deliver more performancefor its deep learning models. On both small and large-scale models. Argonne achieved orders of magnitude performance improvement over GPUs throughSambaNova’s naturally scalable system.

Discover What’s Possible With Next-Generation AI

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SE C T ION 6

Level the Playing Field With
Next-Generation AI

123

SambaNova, through its flagship DataScale

Dataflow-as-a-Service opens up a newWith state-of-the-art accuracy, performance,hardware architecture, has optimized dataflow

realm of digital transformation available toscale, and ease of use, SambaNova hasfrom silicon to algorithms and has achieved

everyone. Now all organizations can accesspositioned itself as the definitive leader inrecord-breaking performance and accuracy.

accurate and powerful AI solutions at lessthe next generation of AI.

cost and complexity.

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L E A R N H OW YOUR OR GANIZ ATION CAN HAR NES S DATAF LOW
TO R E DEF INE YOUR IND US TRY — AND THE WOR LD.

Visit us on the web at SambaNova.AI

SambaNova Systems is an AI innovation company that empowers organizations to deploy best-in-class solutions for computer vision, natural language processing, recommendation systems, andAI for science with confidence. SambaNova’s flagship offering, Dataflow-as-a-Service, helps organizations rapidly deploy AI in days, unlocking new revenue and boosting operational efficiency.
SambaNova’s DataScale® is an integrated software and hardware system using Reconfigurable Dataflow Architecture (RDA), along with open standards and user interfaces. Headquartered in Palo
Alto, California, SambaNova Systems was founded in 2017 by industry luminaries, hardware, and software design experts from Sun/Oracle and Stanford University. Investors include SoftBank Vision
Fund 2, funds and accounts managed by BlackRock, Intel Capital, GV, Walden International, Temasek, GIC, Redline Capital, Atlantic Bridge Ventures, Celesta, and several others. For more
information, please visit us at sambanova.ai or contact us at info@sambanova.ai. Follow SambaNova Systems on LinkedIn.

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