In this paper, we present a two-speed, radix-4, serial-parallel multiplier for accelerating applications such as digital filters, artificial neural networks, and other machine learning algorithms. Our multiplier is a variant of the serial–parallel (SP) modified radix-4 Booth multiplier that adds only the nonzero Booth encodings and skips over the zero operations, making the latency dependent on the multiplier value. Two subcircuits with different critical paths are utilized so that throughput and latency are improved for a subset of multiplier values. The multiplier is evaluated on an Intel Cyclone V field-programmable gate array against standard parallel–parallel and SP multipliers across four different process–voltage–temperature corners. We show that for bit widths of 32 and 64, our optimizations can result in a 1.42×–3.36× improvement over the standard parallel Booth multiplier in terms of area–time depending on the input set.