While much of the tech world remains fixated on the latest large language models (LLMs) powered by Nvidia GPUs, a quieter revolution is brewing in AI hardware. As the limitations and energy demands of traditional deep learning architectures become increasingly apparent, a new paradigm called neuromorphic computing is emerging – one that promises to slash the computational and power requirements of AI by orders of magnitude. To delve into this promising technology, VentureBeat spoke with Sumeet Kumar, CEO and founder of Innatera, a leading startup in the neuromorphic chip space.
“Neuromorphic processors are designed to mimic the way biological brains process information,” Kumar explained. “Rather than performing sequential operations on data stored in memory, neuromorphic chips use networks of artificial neurons that communicate through spikes, much like real neurons.” This brain-inspired architecture gives neuromorphic systems distinct advantages, particularly for edge computing applications in consumer devices and industrial IoT.
Kumar highlighted several compelling use cases, including always-on audio processing for voice activation, real-time sensor fusion for robotics and autonomous systems, and ultra-low power computer vision. “The key is that neuromorphic processors can perform complex AI tasks using a fraction of the energy of traditional solutions,” Kumar noted. “This enables capabilities like continuous environmental awareness in battery-powered devices that simply weren’t possible before.”
Innatera’s flagship product, the Spiking Neural Processor T1, unveiled in January 2024, exemplifies these advantages. The T1 combines an event-driven computing engine with a conventional CNN accelerator and RISC-V CPU, creating a comprehensive platform for ultra-low-power AI in battery-powered devices. “Our neuromorphic solutions can perform computations with 500 times less energy compared to conventional approaches,” Kumar stated. “And we’re seeing pattern recognition speeds about 100 times faster than competitors.”
Kumar illustrated this point with a real-world application. Innatera has partnered with Socionext, a Japanese sensor vendor, to develop an innovative solution for human presence detection. This technology, demonstrated at CES in January, combines a radar sensor with Innatera’s neuromorphic chip to create highly efficient, privacy-preserving devices. “Take video doorbells, for instance,” Kumar explained. “Traditional ones use power-hungry image sensors that need frequent recharging. Our solution uses a radar sensor, which is far more energy-efficient.” The system can detect human presence even when a person is motionless, as long as they have a heartbeat. Being non-imaging, it preserves privacy until it’s necessary to activate a camera. This technology has wide-ranging applications beyond doorbells, including smart home automation, building security, and occupancy detection in vehicles.
“These dramatic improvements in energy efficiency and speed are driving significant industry interest,” Kumar revealed. Innatera has multiple customer engagements, with traction for neuromorphic technologies growing steadily. The company is targeting the sensor-edge applications market, with an ambitious goal of bringing intelligence to a billion devices by 2030. To meet this growing demand, Innatera is ramping up production. The Spiking Neural Processor is slated to enter production later in 2024, with high-volume deliveries starting in Q2 of 2025.
This timeline reflects the rapid progress the company has made since spinning out from Delft University of Technology in 2018. In just six years, Innatera has grown to about 75 employees and recently appointed Duco Pasmooij, former VP at Apple, to their advisory board. The company recently closed a $21 million Series A round to accelerate the development of its spiking neural processors. The round, which was oversubscribed, included investors like Innavest, InvestNL, EIC Fund, and MIG Capital. This strong investor backing underscores the growing excitement around neuromorphic computing.
Kumar envisions a future where neuromorphic chips increasingly handle AI workloads at the edge, while larger foundational models remain in the cloud. “There’s a natural complementarity,” he said. “Neuromorphics excel at fast, efficient processing of real-world sensor data, while large language models are better suited for reasoning and knowledge-intensive tasks.”
“It’s not just about raw computing power,” Kumar observed. “The brain achieves remarkable feats of intelligence with a fraction of the energy our current AI systems require. That’s the promise of neuromorphic computing – AI that’s not only more capable but dramatically more efficient.” Kumar emphasized a key factor that could accelerate the adoption of their neuromorphic technology: developer-friendly tools. “We’ve built a very extensive software development kit that allows application developers to easily target our silicon,” Kumar explained.
Innatera’s SDK uses PyTorch as a front end. “You actually develop your neural networks completely in a standard PyTorch environment,” Kumar noted. “So if you know how to build neural networks in PyTorch, you can already use the SDK to target our chips.” This approach significantly lowers the barrier to entry for developers already familiar with popular machine learning frameworks. It allows them to leverage their existing skills and workflows while tapping into the power and efficiency of neuromorphic computing.
“It is a simple turnkey, standard, and very fast way of building and deploying applications onto our chips,” Kumar added, highlighting the potential for rapid adoption and integration of Innatera’s technology into a wide range of AI applications.
By Impact Lab
