Adaptive Precision CNN Accelerator Using Radix-X Parallel Connected Memristor Crossbars

Understand

Neural processor development is reducing our reliance on remote server access to process deep learning operations in an increasingly edge-driven world.

• By employing in-memory processing, parallelization techniques, and algorithm-hardware co-design, memristor crossbar arrays are known to efficiently compute large scale matrix-vector multiplications.
• However, state-of-the-art implementations of negative weights require duplicative column wires, and high precision weights using single-bit memristors further distributes computations.
• These constraints dramatically increase chip area and resistive losses, which lead to increased power consumption and reduced accuracy.