Princeton Spinout EnCharge AI Raises $21.7M in Series A Funding

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EnCharge AI, a company building advanced computers for AI at the Edge, has raised $21.7 million in series A financing. EnCharge will leverage the funding to further develop and commercialize its semiconductor hardware and software stack.

EnCharge’s charge-based in-memory computing technology was born out of the Defense Advanced Research Projects Agency- and Department of Defense-funded R&D. In the six years since, the company has completed demonstrations of AI performance with orders-of-magnitud-higher compute efficiency and density than has been achieved by both best-in-class digital accelerators, such as GPUs or TPUs, according to EnCharge, as well as recent concepts for beyond-digital accelerators, based on optical or analog computing. Several generations of test chips with end-to-end programmable model execution capability have been demonstrated. EnCharge is targeting applications that have been limited by power and energy requirements and space constraints including automotive sensing, advanced manufacturing, smart retail, smart warehouses and logistics, industrial robotics, and drones, the company said.

EnCharge reported that its test chips and hardware can achieve over 150 TOPS/W for 8-b compute.
The company said that it will will deliver platforms that provide over 20× higher performance per Watt and over 14× higher performance per dollar compared to best-in-class digital AI accelerators implemented in the most advanced technology nodes.

Following this series A round, EnCharge is positioned to develop products to engage with customer applications in production at the forefront of AI, said Naveen Verma, CEO and co-founder of EnCharge AI. The foundational technology for EnCharge’s products was developed at Princeton University, where Verma is a professor of electrical and computer engineering.

Published: December 2022
1. A localized fracture at the end of a cleaved optical fiber or on a glass surface. 2. An integrated circuit.
A semiconductor is a type of material that has electrical conductivity between that of a conductor and an insulator. In other words, semiconductors have properties that are intermediate between metals (good conductors of electricity) and insulators (poor conductors of electricity). The conductivity of a semiconductor can be controlled and modified by factors such as temperature, impurities, or an applied electric field. The most common semiconductors are crystalline solids, and they are...
artificial intelligence
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machine learning
Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to improve their performance on a specific task through experience or training. Instead of being explicitly programmed to perform a task, a machine learning system learns from data and examples. The primary goal of machine learning is to develop models that can generalize patterns from data and make predictions or decisions without being...
machine vision
Machine vision, also known as computer vision or computer sight, refers to the technology that enables machines, typically computers, to interpret and understand visual information from the world, much like the human visual system. It involves the development and application of algorithms and systems that allow machines to acquire, process, analyze, and make decisions based on visual data. Key aspects of machine vision include: Image acquisition: Machine vision systems use various...
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