Tech Alert: BrainChip Showcases Compelling Benchmarks and Recommends Better Metrics for AI Devices at the Edge
BrainChip Holdings Ltd (ASX:BRN)(OTCQX:BRCHF) has published a white paper, 'Benchmarking AI Inference at the Edge,' addressing the current limitations in edge AI benchmarking. The paper highlights the need for metrics that accurately measure performance and efficiency in real-world applications, particularly in industries like automotive and smart homes. It critiques existing benchmarks and suggests enhancements, advocating for a focus on factors such as on-chip computation and model sizes. BrainChip’s Akida™ processor is emphasized for its ability to optimize edge AI performance and efficiency, potentially leading to significant advantages over traditional methods.
- Release of white paper could position BrainChip as a thought leader in edge AI.
- Recommendations for improved metrics may enhance the performance and efficiency of AI applications.
- Akida™ processor demonstrated to maximize edge AI inference performance, suggesting strong competitive advantages.
- Existing benchmarks are criticized for inadequately capturing real-world performance, indicating industry challenges.
- Need for evolving benchmarks suggests current limitations in BrainChip's competitive positioning.
LAGUNA HILLS, CA / ACCESSWIRE / January 15, 2023 / A new white paper by BrainChip Holdings Ltd (ASX:BRN)(OTCQX:BRCHF)(ADR:BCHPY), the world's first commercial producer of ultra-low power, fully digital, event-based, neuromorphic AI IP, evaluates the current state of edge AI benchmarks and the need to continually improve metrics that measure performance and efficiency of real-world, power-conscious edge AI deployments.
Current industry benchmarks measuring edge AI inference performance have started to capture the challenges of edge devices operation over the traditional TOPS rating. The paper, "Benchmarking AI Inference at the Edge: Measuring Performance and Efficiency for Real-World Deployments," recommends the additional factors required to holistically gauge the performance and efficiency needed to enable compelling, optimized AI applications for complex, multi-modal edge environments.
BrainChip's paper examines the limits of conventional AI performance benchmarks; discusses balancing performance and power at the edge; compares performance and energy efficiency with the tinyML benchmarks from MLCommons, which have made a good start towards identifying use cases; and shows how edge AI inference performance and efficiency is maximized with Akida™. It illustrates how various factors like model size, load times and system bandwidth can play a significant part in the overall result but aren't currently accounted for. This is an area where the consortia should, and are, actively collaborating to improve. But there is room for more.
"While there's been a good start, current methods of benchmarking for edge AI don't accurately account for the factors that affect devices in industries such as automotive, smart homes and Industry 4.0," said Anil Mankar, Chief Development Officer at BrainChip. "We believe that as a community, we should evolve benchmarks to continuously incorporate factors such as on-chip, in-memory computation and model sizes to complement the latency and power metrics that are measured today."
To learn more about the importance of balancing these important criteria and better understand how BrainChip's unique approach of event-based, neuromorphic design delivers compelling results, interested parties can download the white paper at https://www.brainchipinc.com/.
About BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY)
BrainChip is the worldwide leader in edge AI on-chip processing and learning. The company's first-to-market, fully digital, event-based AI processor, AkidaTM, uses neuromorphic principles to mimic the human brain, analyzing only essential sensor inputs at the point of acquisition, processing data with unparalleled efficiency, precision, and economy of energy. Akida uniquely enables edge learning local to the chip, independent of the cloud, dramatically reducing latency while improving privacy and data security. Akida Neural processor IP, which can be integrated into SoCs on any process technology, has shown substantial benefits on today's workloads and networks, and offers a platform for developers to create, tune and run their models using standard AI workflows like Tensorflow/Keras. In enabling effective edge compute to be universally deployable across real world applications such as connected cars, consumer electronics, and industrial IoT, BrainChip is proving that on-chip AI, close to the sensor, is the future, for its customers' products, as well as the planet. Explore the benefits of Essential AI at www.brainchip.com.
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Follow BrainChip on LinkedIn: https://www.linkedin.com/company/7792006
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SOURCE: Brainchip Holdings Limited/ADR
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