Baidu Backs Neuromorphic IC Developer

Swiss startup aiCTX has closed a $1.5 million pre-A funding round from Baidu Ventures to develop commercial applications for its low-power neuromorphic computing and processor designs and enable what it calls “neuromorphic intelligence.” It is targeting low-power edge-computing embedded sensory processing systems.

Founded in March 2017 based on advances in neuromorphic computing hardware developed at the Institute of Neuroinformatics of the University of Zurich and ETH Zurich, aiCTX (pronounced “AI-cortex”) is developing “full-stack” custom neuromorphic processors for a variety of artificial-intelligence (AI) edge-computing applications that require ultra-low-power and ultra-low-latency features, including autonomous robots, always-on co-processors for mobile and embedded devices, wearable health-care systems, security, IoT applications, and computing at the network edge.

Dylan Muir, senior R&D engineer at aiCTX, told EE Times that the company is building end-to-end dedicated neuromorphic IP blocks, ASICs, and SoCs as full custom computing solutions that integrate neuromorphic sensors and processors. “This approach ensures minimum size and power consumption and is fundamentally different from most other neuromorphic computing approaches that propose general-purpose solutions as a plug-and-play alternative to parts of machine-learning tool chains with conventional data paths.”

He added, “We engineer spiking neural network and algorithmic solutions that implement computational neuroscience models of cortical computation. Our technology is based on over 20 years of research and development in neuromorphic models of cortical computation that started out at CALTECH in the mid-’90s and are still ongoing at the Institute of Neuroinformatics of the University of Zurich and ETH Zurich.”

Baidu Ventures’ CEO, Wei Liu, said that they invested in aiCTX because it is different from other neuromorphic startups and corporations active in the field in that it has a unique technology and a product-driven focus. “They are developing complete commercial solutions, not simply designing computing fabrics,” he said.

We asked Muir what that meant. He said that, at the moment, other neuromorphic solutions target desktop applications and are based on a standard clocked digital logic design flow. In contrast, aiCTX designs are either based on ultra-low-power mixed-signal analog-digital VLSI circuits or on fully asynchronous low-power hand-crafted digital designs (or both). “We are targeting applications that require ultra-low power (sub-mW to mW) always-on solutions for IoT edge-based computing on mobile and embedded systems that do not need to rely on the cloud,” said Muir.

He added, “We are building demos around those applications and finding potential industrial partners. For example, we’re partnering with a health wearable company to provide ultra-low-power on-board signal processing using our neuromorphic processors.”

Muir said that the company is currently finalizing its new DynapCNN chip, which is a scalable, fully configurable digital event-driven neuromorphic processor with 1M ReLU spiking neurons per chip for implementing spiking convolutional neural networks (sCNN). The chip supports various types of CNN layers (like ReLU, Cropping, Padding, and Pooling) and network architectures (like LeNet, ResNet, and Inception). The technology is aimed at always-on, ultra-low-power and ultra-low-latency event-driven sensory processing applications. Samples of the chip will be available during Q2 of 2019.

In addition, aiCTX said that it is building a new family of neuromorphic chips that combine energy efficiency with features for low-latency, real-time end-to-end applications. The design will provide interfaces for converting analog sensory signals into spikes and for direct event-based input from dynamic-vision sensors, making the devices suitable for mobile health and robotics applications. This neuromorphic processor will tape-out by the end of 2018. The first development kits, along with a software development ecosystem, are planned to be released in Q3 of 2019.

A fully neuromorphic smart vision processor is also under development by a joint venture between aiCTX and neuromorphic vision systems company iniVation. This is a compact, low-cost, single-chip solution for ultra-low-power (sub-mW) and ultra-low-latency (<10 ms) always-on IoT devices and edge-computing vision applications, such as gesture recognition, face or object detection, and surveillance. Samples of smart vision processors are planned for Q4 of 2019.

In terms of business model, aiCTX is developing whole chip solutions for demonstrating and exploring potential applications but, in the long term, hopes to license and provide IP solutions. “The goal is to follow a model similar to that of Arm for the whole IoT edge-computing landscape,” commented Muir. “The IP provided by aiCTX will be tailored exactly to customer and application needs for maximum efficiency.”

Muir said that because the company is developing what it believes is a completely new and disruptive approach to computing, this requires developments at all levels of the hierarchy from the basic computing devices to their design and configuration tools, the high-level algorithmic development, and the testing framework. Now that the optimal solutions and market needs are being identified, aiCTX can expand its chip design and system engineering activities and is starting to talk to investors for a Series A round in the next month. “Baidu Ventures’ investment will help us grow our team, so we can move faster on the applications we’ve identified,” he said.

aiCTX Vision

The company told us that its vision is to develop this technology to solve AI problems and create a whole new field of research that will be called NI (for “neuromorphic intelligence”). Muir said that the landscape of computing is rapidly changing from bulky and power-hungry general-purpose computing systems to small, task-specific, low-power edge-computing embedded sensory processing systems.

The spiking sensory and neural processing systems studied at the Institute of Neuroinformatics of the University of Zurich and ETH Zurich demonstrate that brain-inspired architectures can implement low-power computations efficiently and robustly. “The vision of the company is to exploit the know-how accumulated over the years in studying beyond-von-Neumann computational paradigms and to develop engineering solutions that have high potential in the growing IoT market,” added Muir.

In particular, the last two years have seen tremendous leaps in state-of-the-art neural-network algorithms, especially in the context of application-oriented spiking neural networks. This has paved the path for demonstrable gains in the use of neuromorphic devices for solving complex pattern recognition and classification tasks.

Muir summarized, “Our approach is to deliver not only new hardware tailored for a given application but to provide a full working solution — that means that we also develop neural-network configurations for the neuromorphic devices in-house. We have a research team within aiCTX to build full applications around our neuromorphic hardware.”