aiWare specifications
Key features
| Performance | 1-24 TOPS per core @ 1.5GHz |
| MAC/Cycle (8x8) | 512-4096 INT8 (32-bit internal) |
| Utilization | Up to 98% for NNs such as vgg16 or Yolo >85% for most automotive CNNs |
| NN Convolution | Up to 100% efficiency achievable. MAC arrays optimized for 2D and 3D convolution and deconvolution. Matrix multipliers not used - no need for Winograd or other transforms |
| Support functions | Wide range of Activation, Pooling, Unary, Binary, Tensor Introduction/Shaping and Linear operations to ensure 100% CNN execution within aiWare core with no host CPU intervention |
| Configurability | Wide range of workload optimizations possible without compromising flexibility |
| NN capability | Any INT8 CNN (no depth limit) |
| Data types | INT8 Native 32-bit internal precision and dynamic per-layer scaling |
| Sparsity | Not needed for well-optimized NNs |
| Multicore capability | No limit |
| Safety | Designed to be part of an ASIL-B and higher certified subsystem full safety manual |
Memory
| Embedded SRAM | 10-200 Mbits per core |
| External Memory | Dedicated off-chip DRAM or shared SOC memory |
| Bandwidth reduction | On-chip compression |
| Main interface | AXI4 to LPDDR AXI4 to host |
Neural Network Development Frameworks
| Frameworks supported | Caffe/Caffe2, TensorFlow, PyTorch, ONNX, Khronos NNEF |
| Inference deployment | Binary compiled using aiWare Studio or command line tools offline Single binary contains one or multiple NNs, weights and all scheduling info |
| Software runtime | Minimal host CPU management required during execution. Simple generic portable runtime API runs on any RTOS or HLOS; wrappers to popular APIs available on request |
| Development Tools | aiWare Studio provides comprehensive tools to import, analyse and optimize any NN with easy to use interactive UI |
| Evaluation Tools | aiWare Studio features offline performance estimator accurate to within 5% of final silicon FPGA implementations also available |
Target applications
| Automotive Inference for automated driving | |
| High performance automotive multi-camera perception | |
| Large camera NN processing (no upper limit on input resolution) | |
| High data rate heterogeneous multi-sensor fusion |
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