Hardware Accelerators & Platforms on Embedded Systems Development

NPUs, DSPs & Accelerator Architectures

Mon, 01 Jan 0001 00:00:00 +0000

NPUs, DSPs & Accelerator Architectures#

Neural network inference is dominated by a small set of compute-intensive operations — matrix multiplications, convolutions, and element-wise activations. A single layer of a modest convolutional network may require tens of millions of multiply-accumulate (MAC) operations. Running these on a general-purpose CPU means cycling through scalar or narrow SIMD instructions, burning power on instruction fetch, decode, and branch prediction that contributes nothing to the actual math. Dedicated hardware accelerators exist to collapse these operations into massively parallel, energy-efficient execution — but each accelerator architecture makes different trade-offs in flexibility, model constraints, and power efficiency.

Raspberry Pi AI HAT+

Mon, 01 Jan 0001 00:00:00 +0000

Raspberry Pi AI HAT+#

The Raspberry Pi AI HAT+ adds a Hailo-8L NPU to the Raspberry Pi 5, delivering 13 TOPS of INT8 inference throughput in an M.2 form factor mounted on a HAT+ (Hardware Attached on Top) board. The NPU connects over PCIe Gen 2 x1 — the first Pi to expose a PCIe lane for add-on hardware. This combination transforms the Pi 5 from a platform that struggles with real-time neural network inference into one that runs YOLOv8n object detection at 30+ FPS while the Arm CPU handles camera capture, display, and application logic.

NVIDIA Jetson Orin Nano

Mon, 01 Jan 0001 00:00:00 +0000

NVIDIA Jetson Orin Nano#

The Jetson Orin Nano is NVIDIA’s entry-level module in the Orin family, delivering up to 40 TOPS of AI inference throughput from an Ampere-architecture GPU combined with a Deep Learning Accelerator (DLA). Unlike NPU-based platforms that restrict models to INT8 quantized operators from a fixed set, the Jetson’s GPU-centric architecture runs arbitrary CUDA workloads — meaning virtually any model that can be expressed as a computational graph can execute on this hardware, with TensorRT providing the optimization layer between framework models and GPU execution.

Google Coral Edge TPU

Mon, 01 Jan 0001 00:00:00 +0000

Google Coral Edge TPU#

The Google Coral Edge TPU is a purpose-built ASIC for neural network inference, delivering 4 TOPS of INT8 throughput at just 2 W of power. It occupies the low-power, low-cost end of the hardware accelerator spectrum — simpler and more constrained than the Hailo-8L or Jetson Orin Nano, but extremely efficient for models that fit within its operator and quantization requirements. The key design philosophy is that the Edge TPU runs fully quantized INT8 models with supported operations only — anything outside those bounds falls back to the host CPU, often with dramatic performance consequences.