Data movement in memory-intensive workloads is several orders of magnitude more energy-consuming than computation. Frequent memory-processor transfers make this a critical bottleneck. Processing-using-memory (PuM) alleviates this by enabling in-memory computation and reducing data movement. In this paper, we propose Lama, a LUT-based PuM architecture that efficiently executes SIMD operations by supporting independent column accesses within each mat of a DRAM subarray. Lama minimizes the number of energy-intensive memory activation commands, which are the primary source of overhead in most PuM architectures. Besides, Lama supports up to 8-bit operand precision without decomposing computations, unlike prior PuM solutions, while incurring only a 2.47% area overhead. Our evaluation shows that Lama achieves an average performance improvement of 8.5× over state-of-the-art PuM architectures and a 3.8× improvement over CPU, along with energy efficiency gains of 6.9×/8×mml:mathmml:mrowmml:mo×mml:mo/mml:mn8mml:mo×, respectively, for bulk 8-bit multiplication. We introduce LamaAccel, an HBM-based PuM accelerator that leverages Lama to accelerate LLM inference. By transforming MAC computations into energy-efficient addition and counting operations, LamaAccel achieves up to 9.3×/19.2×mml:mathmml:mrowmml:mo×mml:mo/mml:mn19mml:mo.mml:mn2mml:mo× energy reduction and 4.8×/9.8×mml:mathmml:mrowmml:mo×mml:mo/mml:mn9mml:mo.mml:mn8mml:mo× speedup over a TPU/GPU, as well as up to 5.8× lower energy consumption and 2.1× higher performance compared to a state-of-the-art PuM baseline.
