This paper presents a technique for achieving spatially targeted neural stimulation with suppression of driver nonideality-induced common-mode (CM) artifact in low-latency closed-loop neuromodulation applications. The proposed approach utilizes computationally guided concurrent stimulation across multiple electrodes to achieve spatial selectivity in stimulation. The proposed architecture supports flexible storage of multiple, precomputed vector stimulation patterns in integrated memory. A selected stimulation pattern can be quickly accessed and administered in response to decoded neural activity. Additionally, a combination of the stimulator circuit architecture and mixed-signal current imbalance compensation techniques effectively suppress CM artifacts to below 50 mV. These techniques are demonstrated in a 180 nm HV CMOS test-chip containing 46 stimulation drivers of 26 V compliance and validated through a combination of bench, saline, and in vivo tests.

Stochastic computing (SC) is an alternative computing technique for embedded systems which offers lower area and power, and better error resilience compared to binaryencoded (BE) computation. However, the potential of and general design methodologies for SC in accelerator architectures are not well-understood. In this paper, we evaluate individual SC operations, and end-to-end accelerator architectures to understand when and why SC accelerators can achieve compelling energy efficiency gains. Based on these results, we present general design guidelines that should be considered when building energy-optimal SC accelerator architectures. We also evaluate a fully fabricated ASIC prototype-the first of its kind-to empirically evaluate the error tolerance limits of voltage overscaling (VOS) in SC. Our results show that energy efficiency gains from SC primarily stem from SC's simpler datapaths which require fewer sequential elements compared to BE equivalents. This allows them to achieve energy efficiency gains as high as 2.4x and 30x at 8-bit and 4-bit fixed-point precision, respectively. We also find that VOS can improve the energy efficiency further by up to 1.9x by exploiting SC's error tolerant encoding.