Our contribution demonstrates how developers can easily run neural networks on RISC-V processors using our custom hardware accelerator TetraEdge.
We present a complete solution combining three components.

First, we introduce TetraEdge, a custom hardware SIMD accelerator.
TetraEdge contains a four-stage pipeline design to accelerate 8-bit quantized CNNs inference on 32-bit RISC-V processors.
In comparison to other hardware accelerators, TetraEdge features an innovative automatic data reordering and min/max accumulation.

Second, we extend the NeoRV32 open-source RISC-V processor,
by two custom instructions to control TetraEdge without blocking the main processor.
The CPU continues other tasks while the accelerator handles neural network operations.
By directly interfacing with the CPU core's register file, TetraEdge minimizes area
and control complexity, enabling seamless integration into existing toolchains.

Finally, we combine both aforementioned contributions to the open-source framework AIfES (Artificial Intelligence for Embedded Systems).
AIfES is specifically designed to train and run neural networks directly on resource-constrained devices.
Its modular software architecture enables the integration of user-specific hardware accelerators, such as TetraEdge.
AIfES reduces software overhead significantly with up to 54\% less memory usage and faster execution for CNNs.