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The Origin E1 neural engines by Expedera redefine efficiency and customization for low-power AI solutions. Specially crafted for edge devices like home appliances and security cameras, these engines serve ultra-low power applications that demand continuous sensing capabilities. They minimize power consumption to as low as 10-20mW, keeping data secure and eliminating the need for external memory access. The advanced packet-based architecture enhances performance by facilitating parallel layer execution, thereby optimizing resource utilization. Designed to be a perfect fit for dedicated AI functions, Origin E1 is tailored to support specific neural networks efficiently while reducing silicon area and system costs. It supports various neural networks, from CNNs to RNNs, making it versatile for numerous applications. This engine is also one of the most power-efficient in the industry, boasting an impressive 18 TOPS per Watt. Origin E1 also offers a full TVM-based software stack for easy integration and performance optimization across customer platforms. It supports a wide array of data types and networks, ensuring flexibility and sustained power efficiency, averaging 80% utilization. This makes it a reliable choice for OEMs looking for high performance in always-sensing applications, offering a competitive edge in both power efficiency and security.
The Origin E8 NPUs represent Expedera's cutting-edge solution for environments demanding the utmost in processing power and efficiency. This high-performance core scales its TOPS capacity between 32 and 128 with single-core configurations, addressing complex AI tasks in automotive and data-centric operational settings. The E8’s architecture stands apart due to its capability to handle multiple concurrent tasks without any compromise in performance. This unit adopts Expedera's signature packet-based architecture for optimized parallel execution and resource management, removing the necessity for hardware-specific tweaks. The Origin E8 also supports high input resolutions up to 8K and integrates well across standard and custom neural networks, enhancing its utility in future-forward AI applications. Leveraging a flexible, scalable design, the E8 IP cores make use of an exhaustive software suite to augment AI deployment. Field-proven and already deployed in a multitude of consumer vehicles, Expedera's Origin E8 provides a robust, reliable choice for developers needing optimized AI inference performance, ideally suited for data centers and high-power automobile systems.
Origin E2 NPUs focus on delivering power and area efficiency, making them ideal for on-device AI applications in smartphones and edge nodes. These processing units support a wide range of neural networks, including video, audio, and text-based applications, all while maintaining impressive performance metrics. The unique packet-based architecture ensures effective performance with minimal latency and eliminates the need for hardware-specific optimizations. The E2 series offers customization options allowing it to fit specific application needs perfectly, with configurations supporting up to 20 TOPS. This flexibility represents significant design advancements that help increase processing efficiency without introducing latency penalties. Expedera's power-efficient design results in NPUs with industry-leading performance at 18 TOPS per Watt. Further augmenting the value of E2 NPUs is their ability to run multiple neural network types efficiently, including LLMs, CNNs, RNNs, and others. The IP is field-proven, deployed in over 10 million consumer devices, reinforcing its reliability and effectiveness in real-world applications. This makes the Origin E2 an excellent choice for companies aiming to enhance AI capabilities while managing power and area constraints effectively.
The TimbreAI T3 is an ultra-low-power AI engine specifically designed for audio applications, providing optimal performance in noise reduction tasks for devices like headsets. Known for its energy efficiency, the T3 operates with less than 300 microwatts of power consumption, allowing it to support performance-hungry applications without requiring external memory. The innovative architecture of TimbreAI leverages a packet-based framework focusing on achieving superior power efficiency and customization to the specific requirements of audio neural networks. This tailored engineering ensures no alteration is needed in trained models to achieve the desired performance metrics, thereby establishing a new standard in energy-efficient AI deployments across audio-centric devices. Geared towards consumer electronics and wearables, the T3 extends the potential for battery life in TWS headsets and similar devices by significantly reducing power consumption. With its preconfiguration for handling common audio network functions, TimbreAI provides a seamless development environment for OEMs eager to integrate AI capabilities with minimal power and area overheads.
The Origin E6 neural engines are built to push the boundaries of what's possible in edge AI applications. Supporting the latest in AI model innovations, such as generative AI and various traditional networks, the E6 scales from 16 to 32 TOPS, aimed at balancing performance, efficiency, and flexibility. This versatility is essential for high-demand applications in next-generation devices like smartphones, digital reality setups, and consumer electronics. Expedera’s E6 employs packet-based architecture, facilitating parallel execution that leads to optimal resource usage and eliminating the need for dedicated hardware optimizations. A standout feature of this IP is its ability to maintain up to 90% processor utilization even in complex multi-network environments, thus proving its robustness and adaptability. Crafted to fit various use cases precisely, E6 offers a comprehensive TVM-based software stack and is well-suited for tasks that require simultaneous running of numerous neural networks. This has been proven through its deployment in over 10 million consumer units. Its design effectively manages power and system resources, thus minimizing latency and maximizing throughput in demanding scenarios.
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