CVMAX: Accelerator Architecture with Polar Form Multiplication for Complex-Valued Neural Networks

Complex-Valued Neural Networks (CVNNs) have demonstrated high performance in applications where complex numbers are essential, but suffer from higher computational and memory overheads. Since their target applications often operate in resource-constrained environments, optimizing CVNNs for energy and area efficiency is important for their acceleration. To resolve these challenges, we present CVMAX, a software-hardware co-design for energy and area-efficient CVNN acceleration. CVMAX introduces a specialized quantization technique based on polar form representation and shift quantization. The technique significantly reduces the bit width of CVNNs and computational complexity compared to conventional quantization with rectangular form. Moreover, shift quantization leverages the computational simplicity of multiplication in polar form, reducing the complexity of complex number multiplication. With the quantization technique, we designed a dedicated hardware accelerator that supports CVMAX data and its associated arithmetic operations. In our evaluation, CVMAX achieves a 75% reduction in energy consumption and achieves a $4.44 \times$ speedup compared to conventional accelerators.