README

Coral NPU

Coral NPU is a hardware accelerator for ML inferencing. Coral NPU is an Open Source IP designed by Google Research and is freely available for integration into ultra-low-power System-on-Chips (SoCs) targeting wearable devices such as hearables, augmented reality (AR) glasses and smart watches.

Coral NPU is a neural processing unit (NPU), also known as an AI accelerator or deep-learning processor. Coral NPU is based on the 32-bit RISC-V Instruction Set Architecture (ISA).

Coral NPU includes three distinct processor components that work together: matrix, vector (SIMD), and scalar.

Coral NPU Archicture Coral NPU Architecture Datasheet

Coral NPU Features

Coral NPU offers the following top-level feature set:

RV32IMF_Zve32x RISC-V instruction set (specifically rv32imf_zve32x_zicsr_zifencei_zbb)
32-bit address space for applications and operating system kernels
Four-stage processor, in-order dispatch, out-of-order retire
Four-way scalar, two-way vector dispatch
128-bit SIMD, 256-bit (future) pipeline
8 KB ITCM memory (tightly-coupled memory for instructions)
32 KB DTCM memory (tightly-coupled memory for data)
Both memories are single-cycle-latency SRAM, more efficient than cache memory
AXI4 bus interfaces, functioning as both manager and subordinate, to interact with external memory and allow external CPUs to configure Coral NPU

System Requirements

Bazel 7.4.1
Python 3.9-3.12 (3.13 support is in progress)
SRecord

Quick Start

# Ensure that test suite passes
bazel run //tests/cocotb:core_mini_axi_sim_cocotb

# Build a binary
bazel build //examples:coralnpu_v2_hello_world_add_floats

# Build the Simulator (non-RVV for shorter build time):
bazel build //tests/verilator_sim:core_mini_axi_sim

# Run the binary on the simulator:
bazel-bin/tests/verilator_sim/core_mini_axi_sim --binary bazel-out/k8-fastbuild-ST-dd8dc713f32d/bin/examples/coralnpu_v2_hello_world_add_floats.elf

Coralnpu

Install / Use

README

Coral NPU

Coral NPU Features

System Requirements

Quick Start

Related Skills