Augmented Reality (AR) System-on-Chips (SoCs) have strict power budgets and form-factor limitations for wearable, all-day use AR glasses running high-performance applications. Limited compute and memory resources that can fit within the strict industrial design area footprint of an AR SoC, however, create performance bottlenecks for demanding workloads such as Pixel Codec Avatars (PiCA) group-calling which connects multiple users with their photorealistic representations. To alleviate this unique wearables challenge, 3D integration with hybrid-bonding technology offers energy-efficient 3D stacking of more silicon resources within the same SoC footprint. Implementing such 3D architectures, however, is another challenge as current EDA tools and flows offer limited 3D design control. In this work, we present a 3D design methodology for robust 3D clock network and datapath design using current EDA tools. To validate the proposed methodology, we implemented a 3D integrated prototype AR SoC housing a 3D-stacked Machine Learning (ML) accelerator utilizing TSMC SoIC™bonding technology. Silicon measurements demonstrate that the 3D ML accelerator enables running PiCA AR group call at 30 frames-per-second (fps) by 3D-expanding its memory resources by 4× to achieve 2× better energy-efficiency when compared to a 2D baseline accelerator at iso-footprint.

In the early design phase of a Deep Neural Network (DNN) acceleration system, fast energy and latency estimation are important to evaluate the optimality of different design candidates on algorithm, hardware, and algorithm-to-hardware mapping, given the gigantic design space. This work proposes a uniform intra-layer analytical latency model for DNN accelerators that can be used to evaluate diverse architectures and dataflows. It employs a 3-step approach to systematically estimate the latency breakdown of different system components, capture the operation state of each memory component, and identify stall-induced performance bottlenecks. To achieve high accuracy, different memory attributes, operands' memory sharing scenarios, as well as dataflow implications have been taken into account. Validation against an in-house taped-out accelerator across various DNN layers has shown an average latency model accuracy of 94.3%. To showcase the capability of the proposed model, we carry out 3 case studies to assess respectively the impact of mapping, workloads, and diverse hardware architectures on latency, driving design insights for algorithm-hardware-mapping co-optimization.

Increasing the capacity of the Last Level Cache (LLC) can help scale the memory wall. Due to prohibitive area and leakage power, however, growing conventional SRAM LLC already incurs diminishing returns. Emerging Non-Volatile Memory (NVM) technologies like Spin Torque Transfer RAM (STTRAM) promise high density and low leakage, thereby offering an attractive alternative for building large capacity LLCs. However these technologies have significantly longer write latency compared to SRAM, which interferes with reads and severely limits their performance potential. Despite the recent work showing the write latency reduction at NVM technology level, practical considerations like high yield and low bit error rates will result a significant loss of NVM density when these techniques are implemented. Therefore, improving the write latency while compromising on the density results in sub-optimal usage of the NVM technology. In this paper we present a novel STTRAM LLC design that mitigates the long write latency, thereby delivering SRAM like performance while preserving the benefits of high density. Based on a light-weight learning mechanism, our solution relieves LLC congestion through two schemes. Firstly, we propose write congestion aware bypass that eliminates a large fraction of writes. Despite dropping LLC hit rates which could severely degrade performance in a conventional LLC, our policy smartly modulates the bypass, overcomes the hit rate loss and delivers significant performance gain. Furthermore, our solution establishes a virtual hybrid cache that absorbs and eliminates the redundant writes, which otherwise might be repeatedly and slowly written to the NVM LLC. Detailed simulation of traditional SPEC CPU 2006 suite as well as important industry workloads running on a 4-core system shows that our proposal delivers on an average 26% performance improvement over a baseline LLC design using 8MB STTRAM, while reducing the memory system energy by 10%. Our design outperforms a similar area SRAM LLC by nearly 18%, thereby making NVM technology an attractive alternative for future high performance computing.

Self-powered systems running on scavenged energy will be a key enabler for pervasive computing across the Internet of Things. The variability of input power in energy-harvesting systems limits the effectiveness of static optimizations aimed at maximizing the input-energy-to-computation ratio. We show that the resultant gap between available and exploitable energy is significant, and that energy storage optimizations alone do not significantly close the gap. We characterize these effects on a real, fabricated energy-harvesting system based on a nonvolatile processor. We introduce a unified energy-oriented approach to first optimize the number of backups, by more aggressively using the stored energy available when power failure occurs, and then optimize forward progress via improving the rate of input energy to computation via dynamic voltage and frequency scaling and self-learning techniques. We evaluate combining these schemes and show capture of up to 75.5% of all input energy toward processor computation, an average of 1.54 × increase over the best static “Forward Progress” baseline system. Notably, our energy-optimizing policy combinations simultaneously improve both the rate of forward progress and the rate of backup events (by up to 60.7% and 79.2% for RF power, respectively, and up to 231.2% and reduced to zero, respectively, for solar power). This contrasts with static frequency optimization approaches in which these two metrics are antagonistic.

Steep-slope heterojunction tunnel field-effect transistor (HTFET) devices promise new opportunities beyond CMOS in low-power high-performance communication applications. In this paper, the circuit design optimization of a low-power 14-bit 1-GS/s current-steering digital-to-analog converter (DAC) using 0.4/0.3 V mixed-supply HTFETs is explored. Based on the device characteristics comparison and circuit analysis, it is shown in this paper that HTFET endorses significant differences in both I -V and C -V due to the steep-slope tunneling mechanism and a nature of vertically fabricated structure. While such differences significantly affect the circuit design corners, this paper gives the device-circuit co-optimization for the HTFET DAC, reaching at higher current source output impedance, less nonlinear switching glitch distortions, and thus superior spectral performance over the Si-CMOS DAC. HTFET device variation is also discussed, and calibration techniques are adopted for the static matching accuracy.

The existence of domains where traditional CMOS processors are inefficient has been well-documented in the current literature. In particular, the inefficiency of general purpose CMOS designs operating at very low supply voltages is well-known, and steep sub-threshold slope technologies, such as Tunneling Field Effect Transistors (TFETs), have been demonstrated as a viable alternative for the low-voltage operation domain. However, restricting the design space of steep slope technology-based processors to near-threshold or sub-threshold general purpose processors does the technology a disservice.

For any given application, there is an optimal throughput point in the space of per-processor performance and the number of such processors given to that application. However, due to thermal, yield, and other constraints, not all of these optimal points can plausibly be constructed with a given technology. In this paper, we look at how emerging steep slope devices, 3D circuit integration, and trends in process technology scaling will combine to shift the boundaries of both attainable performance, and the optimal set of technologies to employ to achieve it. We propose a heterogeneous-technology 3D architecture capable of operating efficiently at an expanded number of points in this larger design space and devise a heterogeneity and thermal aware scheduling algorithm to exploit its potential. Our heterogeneous mapping techniques are capable of producing speedups ranging from 17% for a high end server workloads running at around 90°C to over 160% for embedded systems running below 60°C.

Ultra-low power circuit design techniques have enabled rapid progress in biosignal acquisition. The design of a multi-channel biosignal recording system is a challenging task, considering the low amplitude of neural signals and limited power budget for an implantable system. The front-end low-noise amplifier is a critical component with respect to overall power consumption and noise of such system. In this paper, we present a new design of III-V Heterojunction TFET (HTFET)-based neural amplifier employing a telescopic operational transconductance amplifier (OTA) for multi-channel neural spike recording. Exploiting the unique device characteristics of HTFETs, our simulation shows that the proposed amplifier exhibits a midband gain of 39 dB, a gain bandwidth of 12 Hz-2.1 kHz, and an input-referred noise of 6.27 μVrms, consuming 5 nW of power at a 0.5 V supply voltage. Using the proposed HTFET amplifier, a noise efficiency factor (NEF) of 0.64 is achieved, which is significantly lower than the CMOS-based theoretical limit. Design tradeoffs related to gain, power and noise requirements are investigated, based on a comprehensive electrical noise model of HTFET and compared with the baseline Si FinFET design.

Current mode logic (CML) circuits have been widely used in high-speed data transceivers. The lower-voltage-swing makes the switching speed of CML much higher than the static logic can achieve, so it is worthy to adopt the CML circuits at the cost of higher power consumption in the high-speed applications. In order to obtain a better power efficiency (Frequency/power) in CML, it is critical to reduce the power consumption while maintaining the high operating frequency. This paper proposes an alternative approach by building the CML circuits with tunneling-field-effect-transistor (Tunnel FETs or TFETs) to achieve a high-throughput, low-voltage interface circuit design. By taking advantage of its steep subthreshold slope (less than 60 mV/dec), TFET exhibits the same on/off current ratio at the input voltage swing interval much lower than that of the MOSFETs, which enables the supply voltage scaling in CML circuits. For a design target data-rate (20 Gbps for multiplexer and 50 Gbps for buffer), our simulations show that the proposed TFET CML circuits are able to reduce the supply voltage from 0.6 V in conventional Si FinFET CML circuits to as low as 0.3 V while using the same constant tail current. As a result, a power consumption reduction of approximately 50% is achieved by the proposed TFET CML circuits, making the TFET CML approach a promising candidate for future low-power, high-performance applications.

Steep switching Tunnel FETs (TFET) can extend the supply voltage scaling with improved energy efficiency for both digital and analog/RF application. In this paper, recent approaches on III-V Tunnel FET device design, prototype device demonstration, modeling techniques and performance evaluations for digital and analog/RF application are discussed and compared to CMOS technology. The impact of steep switching, uni-directional conduction and negative differential resistance characteristics are explored from circuit design perspective. Circuit-level implementation such as III-V TFET based Adder and SRAM design shows significant improvement on energy efficiency and power reduction below 0.3V for digital application. The analog/RF metric evaluation is presented including gm/Idsmetric, temperature sensitivity, parasitic impact and noise performance. TFETs exhibit promising performance for high frequency, high sensitivity and ultra-low power RF rectifier application.

Hetero-junction Tunnel FET (HTFET) for ultra-low power RF circuit design has been explored at the device and circuit level. In this paper, benchmarking and design insights for optimizing the performance of the TFET based differential drive rectifier is presented. Our evaluation of the HTFET based rectifier demonstrates its promise compared to the state-of-art passive RFIDs. With the 10-stage optimized TFET rectifier at 915 MHz, PCE of 98% with 0.5 nW power consumption, sensitivity of -24dBm for 9 μW PDCand sensitivity of -33dBm for 0.4μW PDCwere achieved.