Syntiant Core 2 Achieves Lowest Power Results in MLPerf Tiny v1.2 Benchmark Suite
Newly Introduced Low Power Core 3 Architecture Demonstrated at the tinyML Summit April 22-24
Irvine, Calif., April 22, 2024 – Syntiant Corp., the recognized leader in low-power edge AI deployment, today announced that its Core 2 programmable deep learning architecture delivered the lowest power energy performance across three categories in the most recent MLCommons’ MLPerf™ Tiny v1.2 benchmark suite.
Syntiant’s results were performed at two operating points to demonstrate maximum throughput and minimum energy.
In the keyword spotting category in the high-performance setting (1.1V core supply voltage and 98.7MHz clock frequency), Syntiant’s solution delivers 1.5 ms latency while consuming 43.8 uJ/inference. At the low-energy setting (0.9V / 30.7MHz), Core 2 requires 31.5 uJ and 4.4 ms per inference.
The Core 2 delivers 4.1 ms latency while consuming 97.2 uJ/inference at 98.7 MHz in the visual wake words category, while achieving 12.7 ms latency with 71.7 uJ at the low-energy setting.
For image classification workloads, the Core 2 performs at 5.1 ms latency and 139.4 uJ at 98.7 MHz clock frequency. It also achieves 16 ms latency and 101.8 uJ at 30.7MHz.
“Results of the latest MLPerf Tiny benchmark show our Core 2 demonstrated the lowest power across the board and the fasted solution among microcontroller class devices,” said Jeremy Holleman, chief scientist at Syntiant. “The Core 2’s compelling throughput and energy performance further validate the architecture’s versatility across a host of multimodal applications, while supporting the most common network types, ranging from dense and convolution layers to architectures with depth-wise separable layers including MobileNet, to networks with skipped connections such as ResNet.”
Full results of the MLPerf Tiny v1.2 benchmark suite can be downloaded here.
Syntiant will be demonstrating its next-gen Core 3 architecture at the tinyML Summit, being held at the Hyatt Regency San Francisco Airport in Burlingame, Calif., April 22-24, 2024. Contact info@syntiant.com to arrange a meeting or demo.
About MLCommons
MLCommons is an open engineering consortium with a mission to make machine learning better for everyone through benchmarks and data. The MLPerf benchmarks are full system tests that stress machine learning models, software and hardware and optionally measure power usage. The open-source and peer-reviewed benchmark suites provide a level playing field for competition that drives innovation, performance and energy-efficiency for the entire industry. For additional information on MLCommons and details on becoming a Member or Affiliate, visit MLCommons or contact participation@mlcommons.org.
About Syntiant
Founded in 2017 and headquartered in Irvine, Calif., Syntiant Corp. is a leader in delivering hardware and software solutions for edge AI deployment. The company’s purpose-built silicon and hardware-agnostic deep learning models are being deployed globally to power edge AI speech, audio, sensor and vision applications across a wide range of consumer and industrial use cases, from earbuds to automobiles. Syntiant’s advanced chip solutions merge deep learning with semiconductor design to produce ultra-low-power, high performance, deep neural network processors. Syntiant also provides compute-efficient software solutions with proprietary model architectures that enable world-leading inference speed and minimized memory footprint across a broad range of processors. The company is backed by several of the world’s leading strategic and financial investors including Intel Capital, Microsoft’s M12, Applied Ventures, Robert Bosch Venture Capital, the Amazon Alexa Fund and Atlantic Bridge Capital. More information on the company can be found by visiting www.syntiant.com or by following Syntiant on X (formerly Twitter) @Syntiantcorp or LinkedIn.
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