Processor IP brings generative AI to edge devices
BrainChip’s Sequora processor IP core runs generative AI and large language models (LLMs) directly on battery-powered edge devices, such as earbuds and sensor nodes. It pairs with the company’s Akida neural processor IP to handle complementary AI workloads.
Akida processes streaming sensor data, including vibration, audio, radar, raw RF IQ data, and biological signals. Sequora runs generative and sequential AI tasks, from on-device LLM inference to voice-to-voice interaction and AI agents. It is optimized for state-space models (SSMs), which maintain a fixed-size state to represent input history, keeping memory requirements constant regardless of sequence length. This architecture suits generative AI workloads on resource-constrained edge devices.
Sequora uses a unified multiply-accumulate (MAC) array for both signal processing and neural-network computation, eliminating the need for a separate DSP preprocessing stage. Its instruction-set architecture (ISA) also allows new algorithms to be deployed through software updates.
Sequora is available now as licensable semiconductor IP, delivered as process-portable, fully synthesizable RTL. The package includes verification IP, synthesis scripts, and an SDK with a compiler, C++ runtime library, and model zoo.
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