CORDIC Is All You Need
Omkar Kokane, Adam Teman, Anushka Jha, Guru Prasath SL, Gopal Raut, Mukul Lokhande, S. V. Jaya Chand, Tanushree Dewangan, Santosh Kumar Vishvakarma
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Artificial intelligence necessitates adaptable hardware accelerators for efficient high-throughput million operations. We present pipelined architecture with CORDIC block for linear MAC computations and nonlinear iterative Activation Functions (AF) such as tanh, sigmoid, and softmax. This approach focuses on a Reconfigurable Processing Engine (RPE) based systolic array, with 40\% pruning rate, enhanced throughput up to 4.64, and reduction in power and area by 5.02 and 4.06 at CMOS 28 nm, with minor accuracy loss. FPGA implementation achieves a reduction of up to 2.5 resource savings and 3 power compared to prior works. The Systolic CORDIC engine for Reconfigurability and Enhanced throughput (SYCore) deploys an output stationary dataflow with the CAESAR control engine for diverse AI workloads such as Transformers, RNNs/LSTMs, and DNNs for applications like image detection, LLMs, and speech recognition. The energy-efficient and flexible approach extends the enhanced approach for edge AI accelerators supporting emerging workloads.