Year: 2025
Type: preprint
Abstract: 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$\times$, and reduction in power and area by 5.02 $\times$ and 4.06 $\times$ at CMOS 28 nm, with minor accuracy loss. FPGA implementation achieves a reduction of up to 2.5 $\times$ resource savings and 3 $\times$ 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.
Source: arXiv (Cornell University)
Authors: Kokane, Omkar, Teman, Adam, Jha, Anushka, SL, Guru Prasath, Raut, Gopal, Lokhande, Mukul, Chand, S. V. Jaya, Dewangan, Tanushree, Vishvakarma, Santosh Kumar
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6910499160
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