Upgrade the Raspberry Pi for AI with a Neuromorphic Processor
Raspberry Pi developers, makers and hobbyists interested in creating Edge AI use cases need to look no further than the BrainChip neuromorphic add-on cards.
Vendor:
BrainChip Inc.
Category:
Edge AI Accelerator
Neuromorphic Processor IP
Akida is a neural processor platform inspired by the cognitive ability and efficiency of the human brain.
Overview
Akida is a neural processor platform inspired by the cognitive ability and efficiency of the human brain. The Akida 1 platform can independently operate complex inferencing and learning on extremely low-power AI devices, thus delivering highly accurate, intelligent, responsive, real-time applications with greater reliability and security. A scalable, self contained co-processor for advanced neural networks.
Key features
Self-contained neural processor
- Scalable fabric of 1-128 nodes
- Each neural node supports 128MACs
- Configurable 50-130K embedded local SRAM
- DMA for all memory and model operations
- Multi-layer execution without host CPU
- Integrate with ARM, RISC-V with AXI bus interface
Efficient algorithmic mesh
- Convolutional Neural Processor (CNP) and Fully connected Neural Processor (FNP).
Akida efficiently accelerates…
- Image and audio classification
- Object detection
- Scene segmentation
- Gesture and face recognition
- State-of-the-art algorithms in sequence prediction
- Video object detection
- Human action recognition
- Raw-audio classification
- Vital signs prediction
Notable features:
- Supports 4-,2-, and1-bit weights and activations
- Supports multiple layers simultaneously
Software development and deployment:
- Akida leverages standard frameworks and development platforms such as TensorFlow/Keras, and Edge Impulse
- Akida is model-, network-, and OS-agnostic
- BrainChip MetaTF supports model development and optimization for Akida hardware
- Akida model zoo offers a set of pre-built Akida- compatible models, pre-trained weights and training scripts
Block Diagram
Akida 1 block diagram
What’s Included?
- Fully synthesizable RTL.
- IP deliverables package with standard EDA tools.
- Complete test bench with simulation results.
- RTL synthesis scripts and timing constraints. Customized IP packaged targeted for your application.
- Run time software C++ library.
- Processor and OS agnostic.
Specifications
Identity
Part Number
Akida 1
Vendor
BrainChip Inc.
Type
Silicon IP
Files
Note: some files may require an NDA depending on provider policy.
Provider
HQ:
USA
Learn more about Edge AI Accelerator IP core
Pasteur’s Magic Quadrant in AI: The Fusion of Fundamental Research and Practical
In the world of scientific research, few figures embody the fusion of theory and application like Louis Pasteur. His legacy inspired Pasteur’s Quadrant, a framework Donald E. Stokes defined in his 1997 book Pasteur’s Quadrant: Basic Science and Technological Innovation. Stokes showed that the most transformative discoveries arise when research pursues both fundamental understanding and practical impact.
Akida in Space
In this post, we’ll explore how Akida’s unique architecture addresses the energy and latency challenges of space-based AI, and why it’s poised to become a game-changer for edge intelligence beyond Earth.
Akida Exploits Sparsity For Low Power in Neural Networks
In the rapidly evolving field of artificial intelligence, edge computing has become increasingly vital for deploying intelligent systems in real-world environments where power, latency, and bandwidth are limited: we need neural network models to run efficiently.
RISC-V Based TinyML Accelerator for Depthwise Separable Convolutions in Edge AI
While lightweight architectures like MobileNetV2 employ Depthwise Separable Convolutions (DSC) to reduce computational complexity, their multi-stage design introduces a critical performance bottleneck inherent to layer-by-layer execution: the high energy and latency cost of transferring intermediate feature maps to either large on-chip buffers or off-chip DRAM. To address this memory wall, this paper introduces a novel hardware accelerator architecture that utilizes a fused pixel-wise dataflow.
Low-Energy Reduced RISC-V Instruction Subset Processor for Tsetlin Machine Inference at the Edge
In this work, the authors present a domain-specific RISC-V microprocessor architecture and design flow tailored for TM inference. Leveraging the modular structure of RISC-V, we design a reduced instruction subset processor that retains programmability while targeting improved performance and lower energy consumption for TM workloads.
Frequently asked questions about Edge AI Accelerator IP cores
What is Neuromorphic Processor IP?
Neuromorphic Processor IP is a Edge AI Accelerator IP core from BrainChip Inc. listed on Semi IP Hub.
How should engineers evaluate this Edge AI Accelerator?
Engineers should review the overview, key features, supported foundries and nodes, maturity, deliverables, and provider information before shortlisting this Edge AI Accelerator IP.
Can this semiconductor IP be compared with similar products?
Yes. Buyers can compare this product with similar semiconductor IP cores or IP families based on category, provider, process options, and structured technical specifications.