AI for Everyone

Pioneering the next generation of computing, we are building the industry’s most advanced systems platform for running AI applicationsand beyondfrom the datacenter to the cloud, and to the edge.

Software Development is Evolving

The use of machine learning to generate models from data has been replacing the traditional approach of writing explicit instructions for computers. From object detections, to recommendations, to speech synthesis, to language translations, problems that were once challenging to solve using classical programming are now seeing a step-function improvement with machine learning.

The result? A radical shift in where software developers spend their time. Rather than coding algorithms, they now create and curate data sets, getting them to run efficiently with their machine learning models. This creates a completely new paradigm in how software is developed and how the infrastructure required to support it is revolutionizing AI.

End of Multicore Era

Advanced machine learning applications and training deep learning models—with their massive data sets and inference workloads—have outpaced the capabilities of current systems hardware.

Even today’s best-in-class systems, with the largest capacity of compute and memory, cannot proficiently train and complete state-of-the-art models within a manageable amount of time.

To fit within today’s hardware constraints, a significant amount of resources are spent improving the efficiency of the data models—rather than being used to enhance the quality of the models and data sets.

All the while, Moore’s Law is slowing, as it becomes nearly impossible to further increase the density of semiconductor chips.

And though many have tried to create better processors to help meet the compute-intensive requirements of training and machine learning applications, it’s still not enough to advance AI to the next level.

99% of AI applications have yet to be written.

Today’s limited computing infrastructure makes it challenging and costly for even the most innovative companies to succeed with AI.

GPUs are Falling Short

Proficiently training any state-of-the-art deep learning model requires high scale, affordable, and close to real-time speeds.

The size of a neural network is limited by the memory capacity of the hardware used for training. This led to various efforts, many through research, to reduce the model’s complexity and size to ease compute and memory requirements.

At the same time, the advancement of processor and systems technology has plateaued, with engineers at the forefront of AI research constrained by existing hardware options that accidently found their way to becoming AI solutions

A far more efficient systems architecture is needed … a solution built specifically for AI.

Software-Defined Hardware for a New Era of Computing

Software-Defined Hardware (SDH) intelligently configures the hardware to do what a software application needs in the most optimal way. SDH results in orders of magnitude improvements in efficiency that unlocks substantially more compute power to meet the demands of new software development techniques.

Unlike conventional hardware, which presents a set of instruction sets for developers to piece together, SDH enables developers to think from a software-first perspective.

Rather than being constrained by hardware, developers can now ask the question, “What can the hardware do for me?” This enables them to focus on unlocking and discovering new opportunities and accomplish what they once thought impossible.

SambaNova’s approach to building SDH systems is the SambaNova Reconfigurable Dataflow Architecture (RDA).

SambaNova Reconfigurable Dataflow Architecture

SambaNova RDA is a spatially reconfigurable architecture designed to efficiently execute a broad range of AI applications and models of all sizes and forms.

Given any workload, the hardware can natively be optimized and intuitively configured to the optimal data flow for the software. This enables near ASIC-like performance out-of-the-box, without sacrificing programmability and efficiency.

As the state-of-the-art AI algorithms are constantly being redefined, RDA keeps you current with the changing demands.

The software-defined nature of SambaNova RDA eliminates the roadblocks caused by the instruction sets that bottleneck conventional hardware today.

SambaNova DataScale System

SambaNova DataScale future-proofs your datacenter for all workloads—creating an integrated hardware and software platform for attainable AI innovations with limitless possibilities.

If AI is strategic or imperative to your business, SambaNova DataScale provides you with the core infrastructure to run AI applications from the datacenter to the cloud and to the edge.

While others are focusing on one technology component, SambaNova DataScale is built and optimized for data flow
from algorithms to silicon.

Built for AI from the ground up to usher in a new era of computing, SambaNova DataScale empowers you to:

• Run and develop high-performance AI applications at high speed and scale with great efficiency

• Easily deploy the technology, so you can get the most out of the system right away

• Allow your data to flow and to define the performance it needs from the system

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