Eta Compute Announces Production Silicon of the World's Most Energy-efficient Edge AI Processor
Software platform and Processor are ideal for artificial intelligence in edge devices with environmental, sound, motion and image sensors.
WESTLAKE VILLAGE, Calif., February 13, 2020 – Eta Compute Inc., a company dedicated to delivering machine learning to mobile and edge devices using its revolutionary new platform, announces the first shipment of production silicon for its ECM3532, the world’s first AI multicore processor for embedded sensor applications. This unique multicore device features the company’s patented Continuous Voltage Frequency Scaling (CVFS) and delivers power consumption of microwatts for many sensing applications.
Eta Compute’s ECM3532 is a Neural Sensor Processor (NSP) for always-on image and sensor applications. It will be on display at the 2020 tinyML Summit, February 12-13 at Samsung Electronics in San Jose, California. Eta Compute is a Gold Sponsor of tinyML and will demonstrate the ECM3532 for image recognition and other edge sensing applications. The objective of the entire tinyML community is to enable ultra-low power machine learning at the network edge.
“Our Neural Sensor Platform is a complete software and hardware platform that delivers more processing at the lowest power profiles in the industry. This essentially eliminates battery capacity as a barrier to thousands of IoT consumer and industrial applications,” said Ted Tewksbury, CEO of Eta Compute. “We are excited to see the first of many applications our customers are developing come to market later this year.”
Eta Compute’s ECM3532 family brings AI to edge devices and transforms sensor data into actionable information for voice, activity, gesture, sound, image, temperature, pressure, and bio-metrics applications, among others. The platform solves issues for the most important issues in edge computing: lower response time, increased security, and higher accuracy.
“We believe that power consumption, latency and data generation combined with RF transmission are all factors limiting many sensing applications,” said Jim Feldhan, president and founder at Semico Research. “It’s great seeing Eta Compute’s platform coming into the market. Their technology is orders of magnitude more power-efficient than any other technology I have seen to date and it will certainly enable AI at the edge a reality.”
ECM3532
The company’s standalone AI platform includes a multicore processor, that includes flash memory, SRAM, I/O, peripherals and a machine learning software development platform. The patented CVFS substantially increases performance and efficiency for edge devices. The self-timed CVFS architecture allows to automatically and continuously adjusts internal clock rate and supply voltage to maximize energy efficiency for the given workload. The ECM3532 multicore NSP combines an MCU and a DSP, both with CVFS, to optimize execution for the best efficiency making it an ideal solution for IoT sensor nodes.
Key Features:
- 5 x 5 mm 81 ball BGA
- As low as 100μW active power consumption in always-on applications
- Arm Cortex-M3 processor with 256KB SRAM, 512KB Flash
- 16b Dual MAC DSP with 96KB dedicated SRAM for ML acceleration
- Neural Development SDK with TensorFlow interface for seamless model integration into the ECM3532
Partner Quotes
“It’s exciting to see innovative products for low power machine learning being launched at tinyML where experts from the industry, academia, start-ups and government labs share the innovations to drive the whole ecosystem forward,” said Pete Warden, Google Researcher and General Co-chair of the tinyML organization.
“We are amazed by the ECM3532 and its efficiency for machine learning in sensing applications,” said Zach Shelby, CEO of Edge Impulse. “It is an ideal fit for our TinyML lifecycle solution that transforms developers’ abilities to deploy ML for embedded devices by gathering data, building a model that combines signal processing, neural networks and anomaly detection to understand the real world.”
“Himax Imaging HM01B0 and new HM0360 are among the industry’s lowest power image sensors with autonomous operation modes and advanced features to reduce power, latency and system overhead. Our image sensors can operate in sub-mW range and when paired with the low power multi-core processors such as Eta Compute’s ECM3532, developers can quickly deploy edge devices that perform image inference under 1mW,” said Amit Mittra, CTO of Himax Imaging.
For more information visit EtaCompute.com or contact the company via email at info@etacompute.com.
About Eta Compute
Eta Compute delivers the most efficient AI processing which eliminates battery capacity as a barrier for innovative industrial and consumer applications. Our patented Continuous Voltage and Frequency (CVFS) technology results in the world’s lowest power embedded platform and delivers unparalleled machine intelligence for energy-constrained products.
In 2018, the company received BOTH Design Innovation Of The Year and Best Use Of Advanced Technologies awards at ARM TechCon.
|
Related News
- X-Silicon Introduces the World's First Vulkan Driver Implementation for RISC-V, Enabling an entire Ecosystem of 3D Graphics, AI and Compute for Low-Power, Mobile, Edge and IOT Devices
- Unleashing Edge AI Potential: Eta Compute's New Collaboration with NXP Semiconductors
- ARM Launches Cortex-A50 Series, the World's Most Energy-Efficient 64-bit Processors
- World's Most Energy-efficient Processor From ARM Targets Low-Cost MCU, Sensor and Control Markets
- ARM to Reshape the Smartcard Market with Industry's Smallest and Most Energy-Efficient Securcore SC000 Processor
Breaking News
- Jury is out in the Arm vs Qualcomm trial
- Ceva Seeks To Exploit Synergies in Portfolio with Nano NPU
- Synopsys Responds to U.K. Competition and Markets Authority's Phase 1 Announcement Regarding Ansys Acquisition
- Alphawave Semi Scales UCIe™ to 64 Gbps Enabling >20 Tbps/mm Bandwidth Density for Die-to-Die Chiplet Connectivity
- RaiderChip Hardware NPU adds Falcon-3 LLM to its supported AI models
Most Popular
E-mail This Article | Printer-Friendly Page |