Addressing the New Normal with ML at the Embedded Edge
The embedded edge is over $1 trillion market that’s been surviving on old technology for decades. Why don’t we have all of our factories automated with robots, fully autonomous vehicles, or advanced remote medical care? People have been waiting years for these promised technologies and though technical advances have been made, these types of products are still not yet widely available.
The angst in not having these types of products for everyday use has been exacerbated by the COVID-19 pandemic, forcing all of us to adapt to a “new normal”. No one predicted the ripple effect this pandemic would have on society as a whole and on technology in particular. A completely new mindset has emerged on how people interact with the world in every facet of their lives at home, at work, and in society in general.
The pandemic is accelerating large investments in technology and society eagerly awaits these as we all are forced to adapt to this “new normal”. Companies and individuals alike have a heightened sense of urgency to embrace the innovations needed to transform our world to this new reality and quickly embark to upgrade our infrastructure and embedded edge products to address the necessary changes to make this happen.
A key missing enabler is a purpose-built machine learning (ML) platform that contains the necessary technology to help bridge the capability gap to address this massive market and service today’s needs. For this to happen at scale, several elements must come together.
High performance ML at the lowest power possible
To effectively scale embedded edge products beyond just a token number of applications, companies need ML technology that operates at high performance and low power. For example, today’s level 4-5 (L4-L5) autonomous vehicle prototypes consume around 2.5-3KW of power. For L5 autonomous vehicles to become commercially viable, they will need a 30x reduction in power consumption to <100W along with a much smaller physical footprint and a reduced need for active cooling than what is available in today’s prototypes. Not surprisingly, there are no autonomous cars on the road just yet though and the need for these energy efficient vehicles has moved from something that is nice to have to an essential necessity.
Adding ML to embedded edge applications
ML will finally happen at scale in embedded edge applications such as smart vision, robotics, autonomous systems, automotive and aerospace and defense when ML is able to be easily added to entrenched, legacy systems. Fortune 500 companies and startups alike have invested heavily in their current technology platforms. Most of them will not rewrite all their code or completely overhaul their underlying infrastructure to integrate ML. To mitigate risk while reaping the benefits of ML, there needs to be technology that allows for seamless integration of legacy code along with ML into their systems which will create an easy path to develop and deploy these systems.
ML experience with ease of use
Ease of use is extremely important to make ML accessible to the masses. Fortunately, the industry understands this challenge and innovative companies are adopting approaches that allow any model, any neural network, any framework for any workload with any resolution and any frame rate to come in through any sensor and be efficiently compiled by the software and effectively deployed on a purpose-built ML device. This approach accelerates development velocity and eliminates the problem of delivering yesterday’s ML technology in products needed to solve today’s needs.
Democratization of ML development and deployment
Democratization of ML in development and deployment is needed for ML to take off. Previously, products with advanced technologies such as ML were the domain of just a few that had scale and could afford it. However, this now needs to quickly proliferate more broadly – both in development and also in deployment at scale.
Joint innovation is also critical. Thankfully, joint innovation is actively being used to address customer’s real-life systems and applications and advanced machine learning technology is coming just in the nick of time as customers are eagerly awaiting smart products to address the “new normal” at the embedded edge.
(The author Krishna Rangasayee, is the Founder and CEO, SiMa.ai and the views expressed in this article are his own)
