Intra-Body power transfer (IBPT) holds significant potential for powering neural stimulators due to its flexible transmitter placement and extended transmission range. However, the fluctuating transmission channel, varying stimulus demands, and implantation applications pose challenges for the rectifier chip to expand its dynamic range (DR) at high power conversion efficiency (PCE). This paper proposes an NMOS-sided biased rectifier with feedback transistors to clamp the gate voltage at high input power. By properly balancing the forward conduction and reverse leakage current, DR is significantly extended. The chip is fabricated in the 0.18 μm CMOS technology, occupying an area of 444 μm × 403 μm. The post-simulation results confirm that with the frequency and load fixed at 10 MHz and 2 kΩ respectively, the NMOS-sided biased rectifier achieves a DR of 7.5 dB with PCE> 80%.

As the feature size of the semiconductor process is scaling down to 10nm and below, it is possible to assemble systems with high performance processors that can theoretically provide computational power of up to tens of PLOPS. However, the power consumption of these systems is also rocketing up to tens of millions watts, and the actual performance is only around 60% of the theoretical performance. Today, power efficiency and sustained performance have become the main foci of processor designers. Traditional computing architecture such as superscalar and GPGPU are proven to be power inefficient, and there is a big gap between the actual and peak performance. In this paper, we present the MaPU architecture, a novel architecture which is suitable for data-intensive computing with great power efficiency and sustained computation throughput. To achieve this goal, MaPU attempts to optimize the application from a system perspective, including the hardware, algorithm and corresponding program model. It uses an innovative multi-granularity parallel memory system with intrinsic shuffle ability, cascading pipelines with wide SIMD data paths and a state-machine-based program model. When executing typical signal processing algorithms, a single MaPU core implemented with a 40nm process exhibits a sustained performance of 134 GLOPS while consuming only 2.8 W in power, which increases the actual power efficiency by an order of magnitude comparable with the traditional CPU and GPGPU.