The Samsung Exynos family of cores are highperformance “big” processors developed at the Samsung Austin Research & Design Center (SARC) starting in late 2011. This paper discusses selected aspects of the microarchitecture of these cores - specifically perceptron-based branch prediction, Spectre v2 security enhancements, micro-operation cache algorithms, prefetcher advancements, and memory latency optimizations. Each micro-architecture item evolved over time, both as part of continuous yearly improvement, and in reaction to changing mobile workloads.

The AMD Jaguar x86 core uses a fully-synthesized, 128-bit native floating-point unit (FPU) built as a co-processor model. The Jaguar FPU supports several x86 ISA extensions, including x87, MMX, SSE1 through SSE4.2, AES, CLMUL, AVX, and F16C instruction sets. The front end of the unit decodes two complex operations per cycle and uses a dedicated renamer (RN), free list (FL), and retire queue (RQ) for in-order dispatch and retire. The FPU issues to the execution units with a dedicated out-of-order, dual-issue scheduler. Execution units source operands from a synthesized physical register file (PRF) and bypass network. The back end of the unit has two execution pipes: the first pipe contains a vector integer ALU, a vector integer MUL unit, and a floating-point adder (FPA), the second pipe contains a vector integer ALU, a store-convert unit, and a floating-point iterative multiplier (FPM). The implementation of the unit focused on low-power design and on vectorized single-precision (SP) performance optimizations. The verification of the unit required complex pseudo-random and formal verification techniques. The Jaguar FPU is built in a 28nm CMOS process.

Bloom Filters are a technique to reduce the effects of conflicts/interference in hash table-like structures. Conventional hash tables store information in a single location which is susceptible to destructive interference through hash conflicts. A Bloom Filter uses multiple hash functions to store information in several locations, and recombines the information through some voting mechanism. Many microarchitectural predictors use simple single-index hash tables to make binary 0/1 predictions, and Bloom Filters help improve predictor accuracy. However, implementing a true Bloom Filter requires k hash functions, which in turn implies a k-ported hash table, or k sequential accesses. Unfortunately,the area of a hardware table increases quadratically with the port count, increasing costs of area, latency and power consumption. We propose a simple but elegant modification to the Bloom Filter algorithm that uses banking combined with special hash functions that guarantee all hash indexes fall into non-conflicting banks. We evaluate several applications of our Banked Bloom Filter (BBF) prediction in processors: BBF branch prediction, BBF load hit/miss prediction, and BBF last-tag prediction. We show that BBF predictors can provide accurate predictions with substantially less cost than previous techniques.

From multiprocessor scale-up to cache sizes to the number of reorder-buffer entries, microarchitects wish to reap the benefits of more computing resources while staying within power and latency bounds. This tension is quite evident in schedulers, which need to be large and single-cycle for maximum performance on out-of-order cores. In this work we present two straightforward modifications to a matrix scheduler implementation which greatly strengthen its scalability. Both are based on the simple observation that the wakeup and picker matrices are sparse, even at small sizes; thus small indirection tables can be used to greatly reduce their width and latency. This technique can be used to create quicker iso-performance schedulers (17-58% reduced critical path) or larger iso-timing schedulers (7-26% IPC increase). Importantly, the power and area requirements of the additional hardware are likely offset by the greatly reduced matrix sizes and subsuming the functionality of the power-hungry allocation CAMs.

3D die stacking is an exciting new technology that increases transistor density by vertically integrating two or more die with a dense, high-speed interface. The result of 3D die stacking is a significant reduction of interconnect both within a die and across dies in a system. For instance, blocks within a microprocessor can be placed vertically on multiple die to reduce block to block wire distance, latency, and power. Disparate Si technologies can also be combined in a 3D die stack, such as DRAM stacked on a CPU, resulting in lower power higher BW and lower latency interfaces, without concern for technology integration into a single process flow. 3D has the potential to change processor design constraints by providing substantial power and performance benefits. Despite the promising advantages of 3D, there is significant concern for thermal impact. In this research, we study the performance advantages and thermal challenges of two forms of die stacking: Stacking a large DRAM or SRAM cache on a microprocessor and dividing a traditional micro architecture between two die in a stack

Large scheduling windows are an effective mechanism for increasing microprocessor performance through the extraction of instruction level parallelism. Current techniques do not scale effectively for very large windows, leading to slow wakeup and select logic as well as large complicated bypass networks. This paper introduces a new instruction scheduler implementation, referred to as Hierarchical Scheduling Windows or HSW, which exploits latency tolerant instructions in order to reduce implementation complexity. HSW yields a very large instruction window that tolerates wakeup, select, and bypass latency, while extracting significant far flung ILP. Results: It is shown that HSW loses <0.5% performance per additional cycle of bypass/select/wakeup latency as compared to a monolithic window that loses /spl sim/5% per additional cycle. Also, HSW achieves the performance of traditional implementations with only 1/3 to 1/2 the number of entries in the critical timing path.