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An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems

Volkan Cevher, Cosimo Aprile, Yusuf Leblebici, Luca Baldassarre

A power and area effcient hardware encoding system tailored for wireless implantable applications is presented. Constant medical monitoring allowed by implantable devices is the most relevant alternative to current bulky monitoring systems, which, in case of severe mental diseases, require heavy surgery and long term hospitalization periods. In this work, the circuit design and the signal processing algorithm dovetail in order to allow real-time neuronal signal monitoring. Two main features must be met on the circuit level to facilitate the acceptance of the implant from the human body: small area and low power consumption. The presented work proposes a new compression scheme based on the Learning-Based Compressive Subsampling approach, which allows an area reduction with respect to recent published works, while allowing high signal reconstruction quality within low power requirements. The proposed method implements on-the-fly compression coeffcients generation, which does not require large static memories. This new fully digital architecture handles the data compression of each individual neuronal acquisition channel with an area of 200 x 190μ in 0:18 μm CMOS technology, and a power dissipation of only 1:15μW.

2018

Adaptive Learning-Based Compressive Sampling for Low-power Wireless Implants

Volkan Cevher, Catherine Dehollain, Mahsa Shoaran, Cosimo Aprile, Yusuf Leblebici, Luca Baldassarre, Kerim Türe, Gürkan Yilmaz, Franco Maloberti

Implantable systems are nowadays being used to interface the human brain with external devices, in order to understand and potentially treat neurological disorders. The most predominant design constraints are the system’s area and power. In this paper, we implement and combine advanced compressive sampling algorithms to reduce the power requirements of wireless telemetry. Moreover, we apply variable compression, to dynamically modify the device performance, based on the actual signal need. This paper presents an area-efficient adaptive system for wireless implantable devices, which dynamically reduces the power requirements yielding compression rates from 8× to 64×, with a high reconstruction performance, as qualitatively demonstrated on a human data set. Two different versions of the encoder have been designed and tested, one with and the second without the adaptive compression, requiring an area of 230×235 μm and 200 × 190 μm, respectively, while consuming only 0.47 μW at 0.8 V. The system is powered by a 4-coil inductive link with measured power transmission efficiency of 36%, while the distance between the external and internal coils is 10 mm. Wireless data communication is established by an OOK modulated narrowband and an IR-UWB transmitter, while consuming 124.2 pJ/bit and 45.2 pJ/pulse, respectively.

2018

An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems

Volkan Cevher, Cosimo Aprile, Yusuf Leblebici, Luca Baldassarre

A power and area efficient hardware encoding system tailored for wireless implantable applications is presented. Constant medical monitoring allowed by implantable devices is the most relevant alternative to current bulky monitoring systems, which, in case of severe mental diseases, require heavy surgery and long term hospitalization periods. In this work, the circuit design and the signal processing algorithm dovetail in order to allow real-time neuronal signal monitoring. Two main features must be met on the circuit level to facilitate the acceptance of the implant from the human body: small area and low power consumption. The presented work proposes a new compression scheme based on the Learning-Based Compressive Subsampling approach, which allows an area reduction with respect to recent published works, while allowing high signal reconstruction quality within low power requirements. The proposed method implements on-the-fly compression coefficients generation, which does not require large static memories. This new fully digital architecture handles the data compression of each individual neuronal acquisition channel with an area of 200 x 190 mu m in 0.18 mu m, CMOS technology, and a power dissipation of only 1.15 mu W.

ASSOC COMPUTING MACHINERY2018