Automating & Roboticizing Stodgy Industries
Optimizing service-heavy processes
Mutable Software Wrapped Around Off-the-Shelf Hardware
Software to abstract hardware operations and break them down into fundamental constructs like compute, storage, and caching, helps reduce hardware platform lock-in for customers. These innovations have a streamlined path for market adoption.
Platforms like SambaNova Systems abstract out GPUs into a scalable and modular software-led design. This makes the platform scalable to varied deep learning workloads and potentially improves performance in comparison to hardware-led approaches.
Bundling Hardware and Software to Extract Value from New Data Sources
Newly proliferating data sources like sensors with a reduced physical and energy footprint or novel types of bioreactors (e.g. carbon fermenters), create opportunities to drastically improve model performance if harnessed well. One way to do that is to integrate the hardware and software stack into a bundled offering that solves customer painpoints.
For example, LIDAR implemented with off the shelf hardware has enabled the creation of rich spatial maps for diverse environments including forestry, mining, oil and gas, construction, and autonomous vehicles.
The aggregation of IoT endpoints (Integration Platforms as a Service or iPaaS) into supporting platforms, a logical extension of IoT deployments - is inevitable and similar to what happened in scaled dev ops.
Successful newcomers like Golioth (Kubernetes for IoT), offering turnkey cloud connectivity to hardware teams, build sticky iPaaS solutions with high switching costs, due to the strength of the developer community and enterprise near-readiness.
Bringing Compute to the Edge via Software
Edge software innovations increase device workloads and rationalize what information to ingest to make fast decisions at the edge, reduce security risks, and avoid the exponential increase of data roundtripping costs as events proliferate.
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