A 16-channel Real-time Adaptive Neural Signal Compression Engine in 22nm FDSOI

The real-time multi-channel intracranial recording of neural signals is required in both neuroscientific research and clinical practice. Due to the limited power budget and the increasing number of recording channels, a compression engine is highly recommended. This paper proposes a 16-channel realtime adaptive compression engine (ACE) exploiting neural signal properties. It is able to switch between lossless and near-lossless compression modes. In near-lossless mode, it compresses the spike region lossless and discards the rest. The achieved space-saving ratio (SSR) is on average about 62.5% and 91% for lossless and near-lossless modes, respectively. It can save about 78.5% of the power consumption (near-lossless compression) compared to the transmission of raw neural signals. The 16-channel ACE is implemented in 22nm FDSOI technology and consumes 230.0 mu W dynamic- and 55.49 mu W leakage-power at 5 MHz.