Adaptive Receiver Design for High Speed Optical Communication

Conventional input/output (IO) links consume power, independent of changes in the bandwidth demand by the system they are deployed in. As the system is designed to satisfy the peak bandwidth demand, most of the time the IO links are idle but still consuming power. In big data centers, the overall utilization ratio of IO links is less than 10%, corresponding to a large amount of energy wasted for idle operation. This work demonstrates a 60 Gb/s high sensitivity non-return-to-zero (NRZ) optical receiver in 14 nm FinFET technology with less than 7 ns power-on time. The power on time includes the data detection, analog bias settling, photo-diode DC current cancellation, and phase locking by the clock and data recovery circuit (CDR). The receiver autonomously detects the data demand on the link via a proposed link protocol and does not require any external enable or disable signals. The proposed link protocol is designed to minimize the off-state power consumption and power-on time of the link. In order to achieve high data-rate and high-sensitivity while maintaining the power budget, a 1-tap decision feedback equalization method is applied in digital domain. The sensitivity is measured to be -8 dBm, -11 dBm, and -13 dBm OMA (optical modulation amplitude) at 60 Gb/s, 48 Gb/s, and 32 Gb/s data rates, respectively. The energy efficiency in always-on mode is around 2.2 pJ/bit for all data-rates with the help of supply and bias scaling. The receiver incorporates a phase interpolator based clock-and-data recovery circuit with approximately 80 MHz jitter-tolerance corner frequency, thanks to the low-latency full custom CDR logic design. This work demonstrates the fastest ever reported CMOS optical receiver and runs almost at twice the data-rate of the state-of-the-art CMOS optical receiver by the time of the publication. The data-rate is comparable to BiCMOS optical receivers but at a fraction of the power consumption

Memory Systems and Interconnects for Scale-Out Servers

The information revolution of the last decade has been fueled by the digitization of almost all human activities through a wide range of Internet services. The backbone of this information age are scale-out datacenters that need to collect, store, and process massive amounts of data. These datacenters distribute vast datasets across a large number of servers, typically into memory-resident shards so as to maintain strict quality-of-service guarantees. While data is driving the skyrocketing demands for scale-out servers, processor and memory manufacturers have reached fundamental efficiency limits, no longer able to increase server energy efficiency at a sufficient pace. As a result, energy has emerged as the main obstacle to the scalability of information technology (IT) with huge economic implications. Delivering sustainable IT calls for a paradigm shift in computer system design. As memory has taken a central role in IT infrastructure, memory-centric architectures are required to fully utilize the IT's costly memory investment. In response, processor architects are resorting to manycore architectures to leverage the abundant request-level parallelism found in data-centric applications. Manycore processors fully utilize available memory resources, thereby increasing IT efficiency by almost an order of magnitude. Because manycore server chips execute a large number of concurrent requests, they exhibit high incidence of accesses to the last-level-cache for fetching instructions (due to large instruction footprints), and off-chip memory (due to lack of temporal reuse in on-chip caches) for accessing dataset objects. As a result, on-chip interconnects and the memory system are emerging as major performance and energy-efficiency bottlenecks in servers. This thesis seeks to architect on-chip interconnects and memory systems that are tuned for the requirements of memory-centric scale-out servers. By studying a wide range of data-centric applications, we uncover application phenomena common in data-centric applications, and examine their implications on on-chip network and off-chip memory traffic. Finally, we propose specialized on-chip interconnects and memory systems that leverage common traffic characteristics, thereby improving server throughput and energy efficiency