Machine-learning platform offers pretrained models -

Machine-learning platform offers pretrained models

Eta Compute has developed a high-efficiency ASIC and new artificial intelligence (AI) software based on neural networks to solve the problems of edge and mobile devices without the use of cloud resources.

Future mobile devices, which are constantly active in the IoT ecosystem, require a disruptive solution that offers processing power to enable machine intelligence with low power consumption for applications such as speech recognition and imaging.

These are the types of applications for which Eta Compute designed its ECM3531.

The IC is based on the ARM Cortex-M3 and NXP Coolflux DSP processors. It uses a tightly integrated DSP processor and a microcontroller architecture for a significant reduction in power for the intelligence of embedded machines. The SoC includes an analog to digital converter (ADC) sensor interface and highly efficient PMIC circuits. The chip also includes I2C, I2S, GPIOs, RTC, PWM, POR, BOD, SRAM and Flash. The patented hardware architecture (DIAL) is combined with fully customizable CNN-based algorithms to perform machine learning inference in hundreds of microwatts.

The processor, named Tensai, can be used with the popular TensorFlow or Caffe Software. This solution can support a wide range of applications in audio, video and signal processing where power is a strict constraint, such as in UAV (unmanned aerial vehicles) markets, in the Internet of things (IoT) and wearable markets.

ECM3531SP includes pretrained learning machine speech recognition and keyword spotting applications. ECM3531PG pretrained photoplethysmogram (PPG) application and ECM3531SF includes machine algorithms for fusion of gyro, magnometer, and accelerometer sensors (Figure 4).

Figure 4: EMC3531 with its development board (Source: Eta Compute)

The patented hardware architecture is combined with the fully customizable Eta Compute algorithms based on CNN, LSTM, GRU and SNN (spiking neural network) to perform machine learning inference in very few mW. Eta provides kernel software for convolutional neural networks on Coolflux's DSP, which are scalable compared to other NN (neural networks) and which will reduce an additional 30% of the power with asynchronous technology.

Tensai's computational properties offer 30-fold power reduction in a specific CNN-based image classification benchmark — unlike other Cortex-M7-class microcontrollers. Eta Compute has reached 0.04mJ per image out of 8 million operations (figure 5).

Figure 5: accuracy versus SNR (Source: Eta Compute)

The high energy efficiency ASIC and the CNN software developed by Eta Compute avoid the need for numerous training samples for peripheral applications, where the amount of resources (both memory and calculation) is limited. A recent benchmark reached by Eta Compute was an improvement of 2-3 orders of magnitude in the efficiency of the model compared to various variants of neural networks for keyword recognition by consuming only 2 mW of power.

For sensing applications, particularly for motion and environmental sensors, the Eta Compute methodology allows sensor hubs to execute more extensive sensor algorithms by providing data and updates in real time from mobile network devices and the Internet of Things (IoT). The collaboration with Rohm Semiconductor has enabled the development of a Wireless Smart Ubiquitous Network (Wi-SUN) which is compatible with sensor nodes. The nodes will combine Rohm sensor technology and Eta Compute's low-power MCUs to offer solutions for intelligent utility and IoT networks for smart cities. They will be designed for frequent low-latency communications that absorb less than 1 μA during rest and, more importantly, only 1 mA during detection.

Eta Compute believes that neural network technology will play a key role in enabling intelligent peripheral devices. Thanks to the ability to learn and process sensory data directly on the margins in an energy efficient manner, new ASICs will provide relief to the bandwidth requirements needed to send raw data to a cloud-based learning service. The energy efficiency of neuromorphic processors will also allow “always on” solutions without suffering from handicaps deriving from power requirements.

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