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Benchmarks
Language Modeling Evaluation on Eight benchmark LLM tasks
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49,781.23
Throughput (Tokens/s)
Heterogeneous Digital-AIMC framework
-1,192.1028
12,041.3586
25,274.82
38,508.2814
Mar 3, 2026
Throughput (Tokens/s)
Energy Efficiency (Tokens/Watt · s)
Accuracy (1.0x Noise)
Accuracy (1.5x Noise)
Accuracy (2.5x Noise)
Updated 1mo ago
Evaluation Results
Method
Method
Links
Throughput (Tokens/s)
Energy Efficiency (Tokens/Watt · s)
Accuracy (1.0x Noise)
Accuracy (1.5x Noise)
Accuracy (2.5x Noise)
Heterogeneous Digital-AIMC framework
Param. in Digital=5.37...
2026.03
49,781.23
123.92
62.02
60.73
56.71
Heterogeneous Digital-AIMC framework
Param. in Digital=17.0...
2026.03
24,924.12
62.2
62.38
61.49
58.6
Heterogeneous Digital-AIMC framework
Param. in Digital=28.6...
2026.03
14,513.52
36.25
62.5
61.82
59.6
Full Digital (FP-16)
Param. in Digital=100%...
2026.03
4,220.07
10.55
63.17
-
-
Full Analog
Param. in Digital=0% (...
2026.03
768.41
23,949.07
61.05
58.45
49.74
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