IBM analog AI chip
Realizes: Energy-efficient inference via PCM memristive crossbar arrays
IBM Research analog AI chip uses memristive crossbar arrays with PCM elements to implement analog differential compute for neural inference, tightly integrating in-memory multiply-accumulate operations for ultra-low-power AI workloads.
Examples
🔗
IBM analog AI chip research/demo
Tutorial and evaluation of the IBM Analog In-Memory Hardware Acceleration Kit that emulates the analog AI chip pipeline, demonstrating how PCM crossbars can be calibrated for inference and training despite device variability.
Analog matrix-vector multiply-accumulate operations in PCM memristive crossbars for inference/training, showing sub-nJ latency and energy per layer while using differential analog compute.
low-latency
chip-scale
≈0.25 pJ per MAC