In general, the main purpose of using processor-in-memory (PIM) modules is to dramatically increase the data-level parallelism (DLP) and avoid the limited issue rate of current systems (even when they include SIMD extensions) caused by the limited data bandwidth and functional units. Our approach is to divide the PIM module into hundreds of smaller pieces so that each of these smaller PIMs can execute motion estimation for a group of macro blocks in a parallel fashion. We also design the logic in each PIM to execute in a highly pipelined fashion so that even more parallelism can be exploited. The main contribution of this paper is the presentation of architectural techniques that can be used in the PIM module to overcome the addressing and data sharing overhead when these smaller PIMs are used. Our architectural techniques have been applied to motion estimation. Indeed, it has been reported that motion estimation takes the majority of the execution time of MPEG encoding and it has been researched by many because of its importance in MPEG encoding. With our paradigm and techniques, the host processor can be relieved from the most computationally demanding and data-intensive portions of the workload, which should therefore yield a significant performance gain. Indeed, we observed (when 512 of these smaller PIMs were used) a reduction in the number of memory accesses by a factor of up to 2,034 times. At the same time, the performance improved by a multiplicative factor as high as 439 times.

The performance of multiplication is crucial for multimedia applications such as 3D graphics and signal processing systems, which depend on the execution of large numbers of multiplications. Previously reported algorithms mainly focused on rapidly reducing the partial products rows down to final sums and carries used for the final accumulation. These techniques mostly rely on circuit optimization and minimization of the critical paths. In this paper, an algorithm to achieve fast multiplication in two's complement representation is presented. Rather than focusing on reducing the partial products rows down to final sums and carries, our approach strives to generate fewer partial products rows. In turn, this influences the speed of the multiplication, even before applying partial products reduction techniques. Fewer partial products rows are produced, thereby lowering the overall operation time. In addition to the speed improvement, our algorithm results in a true diamond-shape for the partial product tree, which is more efficient in terms of implementation. The synthesis results of our multiplication algorithm using the Artisan TSMC 0.13um 1.2-Volt standard-cell library show 13 percent improvement in speed and 14 percent improvement in power savings for 8-bit \times 8-bit multiplications (10 percent and 3 percent, respectively, for 16-bit \times 16-bit multiplications) when compared to conventional multiplication algorithms.

The performance of multiplication is crucial for multimedia applications such as 3D graphics and signal processing systems which depend on extensive numbers of multiplications. Previously reported multiplication algorithms mainly focus on rapidly reducing the partial products rows down to final sums and carries used for the final accumulation. These techniques mostly rely on circuit optimization and minimization of the critical paths. In this paper, an algorithm to achieve fast multiplication in two's complement representation is presented. Indeed, our approach focuses on reducing the number of partial product rows. In turn, this directly influences the speed of the multiplication, even before applying partial products reduction techniques. Fewer partial products rows are produced, thereby lowering the overall operation time. This results in a true diamond-shape for the partial product tree which is more efficient in terms of implementation.

Motion estimation is the most time consuming stage of MPEG family encodings and it reportedly absorbs up to 90% of the total execution time of MPEG processing. Therefore, we propose a hardware/software co-design paradigm that uses a PIM module to efficiently execute motion estimation operations. We use a PIM module to reduce the memory access penalty caused by a large number of memory accesses. We segment the PIM module into small pieces so that each smaller PIM module can execute the operations in parallel fashion. However, in order to execute the operations in parallel, there are critical overheads that involve replicating a huge amount of data to many of these smaller PIM modules. Not only do these replications require a huge amount of additional memory accesses but also calculations when generating addresses. Therefore, we also present an efficient data distribution mechanism to effectively support parallel executions among these smaller PIM modules. With our paradigm, the host processor can be relieved from computationally-intensive and data-intensive workloads of motion estimation. We observed up to 2034/spl times/ improvement in reduction of the number of memory accesses and up to 439/spl times/ performance improvement for the execution of motion estimation operations when using our computing paradigm.