ISSCC: Deep learning hardware boosts for AI

It noted that: "Deep learning is a rapidly evolving topic, and the computational complexity of typical deep neural networks impedes their execution on resource‑scarce mobile or wearable devices. "Last year, several innovative solutions were introduced to enhance throughput and improve energy efficiency, mostly focusing on the efficiency of convolutional neural networks," it said. "The current state-of-the-art still faces two significant challenges: a need to improve energy efficiency for ultra‑low power applications; and finding solutions for efficient execution of fully connected non‑convolutional networks. To improve energy efficiency, there is a trend towards reduced-precision networks, with binary networks as the extreme case – recently, the first binary neural‑network accelerator has appeared." ISSCC 2018 pushes peak efficiency to several tens of Top/s/W in digital accelerators, and beyond hundreds of Top/s/W for a mixed-signal implementation.