Processors that adapt architecture to workloads at runtime promise compelling performance per watt (PPW) gains, offering one way to mitigate diminishing returns from pipeline scaling. State-of-the-art adaptive CPUs deploy machine learning (ML) models on-chip to optimize hardware by recognizing workload patterns in event counter data. However, despite breakthrough PPW gains, such designs are not yet widely adopted due to the potential for systematic adaptation errors in the field.

We present a compiler for Loihi, a novel manycore neuromorphic processor that features a programmable, on-chip learning engine for training and executing spiking neural networks (SNNs). An SNN is distinguished from other neural networks in that (1) its independent computing units, or "neurons", communicate with others only through spike messages; and (2) each neuron evaluates local learning rules, which are functions of spike arrival and departure timings, to modify its local state. The collective neuronal state dynamics of an SNN form a nonlinear dynamical system that can be cast as an unconventional model of computation. To realize such an SNN on Loihi requires each constituent neuron to locally store and independently update its own spike timing information. However, each Loihi core has limited resources for this purpose and these must be shared by neurons assigned to the same core. In this work, we present a compiler for Loihi that maps the neurons of an SNN onto and across Loihi's cores efficiently. We show that a poor neuron-to-core mapping can incur significant energy costs and address this with a greedy algorithm that compiles SNNs onto Loihi in a power-efficient manner. In so doing, we highlight the need for further development of compilers for this new, emerging class of architectures.

We present a FPGA-synthesizable version of the Intel Nehalem processor core, synthesized, partitioned and mapped to a multi-FPGA emulation system consisting of Xilinx Virtex-4 and Virtex-5 FPGAs. To our knowledge, this is the first time a modern state-of-the-art x86 design with the out-of-order micro-architecture is made FPGA synthesizable and capable of high-speed cycle-accurate emulation. Unlike the Intel Atom core which was made FPGA synthesizable on a single Xilinx Virtex-5 in a previous endeavor, the Nehalem core is a more complex design with aggressive clock-gating, double phase latch RAMs, and RTL constructs that have no true equivalent in FPGA architectures. Despite these challenges, we are successful in making the RTL synthesizable with only 5% RTL code modifications, partitioning the design across five FPGAs, and emulating the core at 520 KHz. The synthesizable Nehalem core is able to boot Linux and execute standard x86 workloads with all architectural features enabled.

We present an FPGA-synthesizable version of the Intel Atom processor core, synthesized to a Virtex-5 based FPGA emulation system. To make the production Atom design in SystemVerilog synthesizable through industry standard EDA tool flow, we transformed and mapped latches in the design, converted clock gating, and replaced nonsynthesizable constructs with FPGA-synthesizable counterparts. Additionally, as the target FPGA emulator is hosted on a PC platform with the Pentium-based CPU socket that supports a significantly different front side bus (FSB) protocol from that of the Atom processor, we replaced the existing bus control logic in the Atom core with an alternate FSB protocol to communicate with the rest of the PC platform. With these efforts, we succeeded in synthesizing the entire Atom processor core to fit within a single Virtex-5 LX330 FPGA. The synthesizable Atom core runs at 50Mhz on the Pentium PC motherboard with fully functional I/O peripherals. It is capable of booting off-the-shelf MS-DOS, Windows XP and Linux operating systems, and executing standard x86 workloads.

Moore's Law and the drive towards performance efficiency have led to the on-chip integration of general-purpose cores with special-purpose accelerators. Pangaea is a heterogeneous CMP design for non-rendering workloads that integrates IA32 CPU cores with non-IA32 GPU-class multi-cores, extending the current state-of-the-art CPU-GPU integration that physically "fuses" existing CPU and GPU designs. Pangaea introduces (1) a resource repartitioning of the GPU, where the hardware budget dedicated for 3D-specific graphics processing is used to build more general-purpose GPU cores, and (2) a 3-instruction extension to the IA32 ISA that supports tighter architectural integration and fine-grain shared memory collaborative multithreading between the IA32 CPU cores and the non-IA32 GPU cores. We implement Pangaea and the current CPU-GPU designs in fully-functional synthesizable RTL based on the production quality RTL of an IA32 CPU and an Intel GMA X4500 GPU. On a 65 nm ASIC process technology, the legacy graphics-specific fixed-function hardware has the area of 9 GPU cores and total power consumption of 5 GPU cores. With the ISA extensions, the latency from the time an IA32 core spawns a GPU thread to the time the thread begins execution is reduced from thousands of cycles to fewer than 30 cycles. Pangaea is synthesized on a FPGA-based prototype and runs off-the-shelf IA32 OSes. A set of general-purpose non-graphics workloads demonstrate speedups of up to 8.8x.

The Multiple Instruction Stream Processor (MISP) architecture introduces the sequencer as a new class of architectural resource, and provides a minimalist user-level MIMD instruction set extension for application programs to directly control execution of concurrent instruction streams on these sequencers. As with classic architectural resources, namely, registers and memory, the sequencer architectural resource can be subject to virtualization. This paper details the idea of Sequencer Virtualization (SV), a foundational architectural support to decouple architectural virtual sequencers from physical sequencers. SV enables more efficient utilization of sequencer resources at the microarchitectural level while maintaining a consistent programming interface at the architectural level. To evaluate the key tradeoffs for SV, we conduct extensive experiments by implementing a prototype SV system using a custom firmware on a large-scale multiprocessor system. Using the prototype SV system, we demonstrate that SV improves efficiency in sequencer utilization while incurring little performance overhead. In particular, for a set of real multithreaded workloads, SV can significantly improve sequencer utilization, achieving an average of 32% better wall-clock performance than MISP without SV support in a multi-programming environment.

Future mainstream microprocessors will likely integrate specialized accelerators, such as GPUs, onto a single die to achieve better performance and power efficiency. However, it remains a keen challenge to program such a heterogeneous multicore platform, since these specialized accelerators feature ISAs and functionality that are significantly different from the general purpose CPU cores. In this paper, we present EXOCHI: (1) Exoskeleton Sequencer(EXO), an architecture to represent heterogeneous acceleratorsas ISA-based MIMD architecture resources, and a shared virtual memory heterogeneous multithreaded program execution model that tightly couples specialized accelerator cores with generalpurpose CPU cores, and (2) C for Heterogeneous Integration(CHI), an integrated C/C++ programming environment that supports accelerator-specific inline assembly and domain-specific languages. The CHI compiler extends the OpenMP pragma for heterogeneous multithreading programming, and produces a single fat binary with code sections corresponding to different instruction sets. The runtime can judiciously spread parallel computation across the heterogeneous cores to optimize performance and power.

Microprocessor design is undergoing a major paradigm shift towards multi-core designs, in anticipation that future performance gains will come from exploiting threadlevel parallelism in the software. To support this trend, we present a novel processor architecture called the Multiple Instruction Stream Processing (MISP) architecture. MISP introduces the sequencer as a new category of architectural resource, and defines a canonical set of instructions to support user-level inter-sequencer signaling and asynchronous control transfer. MISP allows an application program to directly manage user-level threads without OS intervention. By supporting the classic cache-coherent shared-memory programming model, MISP does not require a radical shift in the multithreaded programming paradigm. This paper describes the design and evaluation of the MISP architecture for the IA-32 family of microprocessors. Using a research prototype MISP processor built on an IA-32-based multiprocessor system equipped with special firmware, we demonstrate the feasibility of implementing the MISP architecture. We then examine the utility of MISP by (1) assessing the key architectural tradeoffs of the MISP architecture design and (2) showing how legacy multithreaded applications can be migrated to MISP with relative ease.